diff --git a/python/ray/tune/analysis/experiment_analysis.py b/python/ray/tune/analysis/experiment_analysis.py
index 463d34190..22dba1892 100644
--- a/python/ray/tune/analysis/experiment_analysis.py
+++ b/python/ray/tune/analysis/experiment_analysis.py
@@ -16,8 +16,8 @@ except ImportError:
     DataFrame = None
 
 from ray.tune.error import TuneError
-from ray.tune.result import EXPR_PROGRESS_FILE, EXPR_PARAM_FILE,\
-    CONFIG_PREFIX, TRAINING_ITERATION
+from ray.tune.result import DEFAULT_METRIC, EXPR_PROGRESS_FILE, \
+    EXPR_PARAM_FILE, CONFIG_PREFIX, TRAINING_ITERATION
 from ray.tune.trial import Trial
 from ray.tune.utils.trainable import TrainableUtil
 
@@ -33,7 +33,8 @@ class Analysis:
         experiment_dir (str): Directory of the experiment to load.
         default_metric (str): Default metric for comparing results. Can be
             overwritten with the ``metric`` parameter in the respective
-            functions.
+            functions. If None but a mode was passed, the anonymous metric
+            `ray.tune.result.DEFAULT_METRIC` will be used per default.
         default_mode (str): Default mode for comparing results. Has to be one
             of [min, max]. Can be overwritten with the ``mode`` parameter
             in the respective functions.
@@ -57,6 +58,10 @@ class Analysis:
                 "`default_mode` has to be None or one of [min, max]")
         self.default_mode = default_mode
 
+        if self.default_metric is None and self.default_mode:
+            # If only a mode was passed, use anonymous metric
+            self.default_metric = DEFAULT_METRIC
+
         if not pd:
             logger.warning(
                 "pandas not installed. Run `pip install pandas` for "
diff --git a/python/ray/tune/function_runner.py b/python/ray/tune/function_runner.py
index 9fa19e6d2..f9938bc2d 100644
--- a/python/ray/tune/function_runner.py
+++ b/python/ray/tune/function_runner.py
@@ -16,8 +16,8 @@ from six.moves import queue
 from ray.util.debug import log_once
 from ray.tune import TuneError, session
 from ray.tune.trainable import Trainable, TrainableUtil
-from ray.tune.result import (TIME_THIS_ITER_S, RESULT_DUPLICATE,
-                             SHOULD_CHECKPOINT)
+from ray.tune.result import (DEFAULT_METRIC, TIME_THIS_ITER_S,
+                             RESULT_DUPLICATE, SHOULD_CHECKPOINT)
 from ray.tune.utils import (detect_checkpoint_function, detect_config_single,
                             detect_reporter)
 
@@ -164,7 +164,7 @@ class StatusReporter:
             "report __call__ is made to ensure correct runtime metrics.")
 
         if _metric:
-            kwargs["_metric"] = _metric
+            kwargs[DEFAULT_METRIC] = _metric
 
         # time per iteration is recorded directly in the reporter to ensure
         # any delays in logging results aren't counted
diff --git a/python/ray/tune/progress_reporter.py b/python/ray/tune/progress_reporter.py
index 8ff397237..59c540dd8 100644
--- a/python/ray/tune/progress_reporter.py
+++ b/python/ray/tune/progress_reporter.py
@@ -6,9 +6,9 @@ import sys
 import numpy as np
 import time
 
-from ray.tune.result import (EPISODE_REWARD_MEAN, MEAN_ACCURACY, MEAN_LOSS,
-                             TRAINING_ITERATION, TIME_TOTAL_S, TIMESTEPS_TOTAL,
-                             AUTO_RESULT_KEYS)
+from ray.tune.result import (DEFAULT_METRIC, EPISODE_REWARD_MEAN,
+                             MEAN_ACCURACY, MEAN_LOSS, TRAINING_ITERATION,
+                             TIME_TOTAL_S, TIMESTEPS_TOTAL, AUTO_RESULT_KEYS)
 from ray.tune.trial import Trial
 from ray.tune.utils import unflattened_lookup
 
@@ -135,6 +135,10 @@ class TuneReporterBase(ProgressReporter):
         if mode:
             self._mode = mode
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         return True
 
     def set_total_samples(self, total_samples):
diff --git a/python/ray/tune/result.py b/python/ray/tune/result.py
index 7aedc2837..e9eb7f402 100644
--- a/python/ray/tune/result.py
+++ b/python/ray/tune/result.py
@@ -23,6 +23,9 @@ NODE_IP = "node_ip"
 # (Auto-filled) The pid of the training process.
 PID = "pid"
 
+# (Optional) Default (anonymous) metric when using tune.report(x)
+DEFAULT_METRIC = "_metric"
+
 # (Optional) Mean reward for current training iteration
 EPISODE_REWARD_MEAN = "episode_reward_mean"
 
diff --git a/python/ray/tune/schedulers/async_hyperband.py b/python/ray/tune/schedulers/async_hyperband.py
index 375245fb9..bfd683963 100644
--- a/python/ray/tune/schedulers/async_hyperband.py
+++ b/python/ray/tune/schedulers/async_hyperband.py
@@ -4,6 +4,7 @@ from typing import Dict, Optional, Union
 import numpy as np
 
 from ray.tune import trial_runner
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
 from ray.tune.trial import Trial
 
@@ -26,7 +27,8 @@ class AsyncHyperBandScheduler(FIFOScheduler):
             `training_iteration` as a measure of progress, the only requirement
             is that the attribute should increase monotonically.
         metric (str): The training result objective value attribute. Stopping
-            procedures will use this attribute.
+            procedures will use this attribute. If None but a mode was passed,
+            the `ray.tune.result.DEFAULT_METRIC` will be used per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         max_t (float): max time units per trial. Trials will be stopped after
@@ -103,6 +105,10 @@ class AsyncHyperBandScheduler(FIFOScheduler):
         elif self._mode == "min":
             self._metric_op = -1.
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         return True
 
     def on_trial_add(self, trial_runner: "trial_runner.TrialRunner",
diff --git a/python/ray/tune/schedulers/hyperband.py b/python/ray/tune/schedulers/hyperband.py
index 46cb7a81b..a7e31fc6a 100644
--- a/python/ray/tune/schedulers/hyperband.py
+++ b/python/ray/tune/schedulers/hyperband.py
@@ -5,6 +5,7 @@ import numpy as np
 import logging
 
 from ray.tune import trial_runner
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
 from ray.tune.trial import Trial
 from ray.tune.error import TuneError
@@ -64,7 +65,8 @@ class HyperBandScheduler(FIFOScheduler):
             `training_iteration` as a measure of progress, the only requirement
             is that the attribute should increase monotonically.
         metric (str): The training result objective value attribute. Stopping
-            procedures will use this attribute.
+            procedures will use this attribute. If None but a mode was passed,
+            the `ray.tune.result.DEFAULT_METRIC` will be used per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         max_t (int): max time units per trial. Trials will be stopped after
@@ -138,6 +140,10 @@ class HyperBandScheduler(FIFOScheduler):
         elif self._mode == "min":
             self._metric_op = -1.
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         return True
 
     def on_trial_add(self, trial_runner: "trial_runner.TrialRunner",
diff --git a/python/ray/tune/schedulers/median_stopping_rule.py b/python/ray/tune/schedulers/median_stopping_rule.py
index b446547ec..12eb956cd 100644
--- a/python/ray/tune/schedulers/median_stopping_rule.py
+++ b/python/ray/tune/schedulers/median_stopping_rule.py
@@ -5,6 +5,7 @@ from typing import Dict, List, Optional
 import numpy as np
 
 from ray.tune import trial_runner
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.trial import Trial
 from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
 
@@ -22,7 +23,8 @@ class MedianStoppingRule(FIFOScheduler):
             `training_iteration` as a measure of progress, the only requirement
             is that the attribute should increase monotonically.
         metric (str): The training result objective value attribute. Stopping
-            procedures will use this attribute.
+            procedures will use this attribute. If None but a mode was passed,
+            the `ray.tune.result.DEFAULT_METRIC` will be used per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         grace_period (float): Only stop trials at least this old in time.
@@ -93,6 +95,10 @@ class MedianStoppingRule(FIFOScheduler):
         self._worst = float("-inf") if self._mode == "max" else float("inf")
         self._compare_op = max if self._mode == "max" else min
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         return True
 
     def on_trial_add(self, trial_runner: "trial_runner.TrialRunner",
diff --git a/python/ray/tune/schedulers/pbt.py b/python/ray/tune/schedulers/pbt.py
index 20d12819a..96dbd7cbc 100644
--- a/python/ray/tune/schedulers/pbt.py
+++ b/python/ray/tune/schedulers/pbt.py
@@ -10,7 +10,7 @@ from typing import Callable, Dict, List, Optional, Tuple, Union
 from ray.tune import trial_runner
 from ray.tune import trial_executor
 from ray.tune.error import TuneError
-from ray.tune.result import TRAINING_ITERATION
+from ray.tune.result import DEFAULT_METRIC, TRAINING_ITERATION
 from ray.tune.utils.util import SafeFallbackEncoder
 from ray.tune.sample import Domain, Function
 from ray.tune.schedulers import FIFOScheduler, TrialScheduler
@@ -141,7 +141,8 @@ class PopulationBasedTraining(FIFOScheduler):
             `training_iteration` as a measure of progress, the only requirement
             is that the attribute should increase monotonically.
         metric (str): The training result objective value attribute. Stopping
-            procedures will use this attribute.
+            procedures will use this attribute. If None but a mode was passed,
+            the `ray.tune.result.DEFAULT_METRIC` will be used per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         perturbation_interval (float): Models will be considered for
@@ -310,6 +311,10 @@ class PopulationBasedTraining(FIFOScheduler):
         elif self._mode == "min":
             self._metric_op = -1.
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         return True
 
     def on_trial_add(self, trial_runner: "trial_runner.TrialRunner",
diff --git a/python/ray/tune/schedulers/trial_scheduler.py b/python/ray/tune/schedulers/trial_scheduler.py
index 56df73943..2dc4f918b 100644
--- a/python/ray/tune/schedulers/trial_scheduler.py
+++ b/python/ray/tune/schedulers/trial_scheduler.py
@@ -1,6 +1,7 @@
 from typing import Dict, Optional
 
 from ray.tune import trial_runner
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.trial import Trial
 
 
@@ -32,6 +33,11 @@ class TrialScheduler:
             return False
         if metric:
             self._metric = metric
+
+        if self._metric is None:
+            # Per default, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         return True
 
     def on_trial_add(self, trial_runner: "trial_runner.TrialRunner",
diff --git a/python/ray/tune/suggest/ax.py b/python/ray/tune/suggest/ax.py
index 8cc6949ff..e2540fe0a 100644
--- a/python/ray/tune/suggest/ax.py
+++ b/python/ray/tune/suggest/ax.py
@@ -1,6 +1,7 @@
 from typing import Dict, List, Optional, Union
 
 from ax.service.ax_client import AxClient
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Categorical, Float, Integer, LogUniform, \
     Quantized, Uniform
 from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
@@ -45,7 +46,8 @@ class AxSearch(Searcher):
         metric (str): Name of the metric used as objective in this
             experiment. This metric must be present in `raw_data` argument
             to `log_data`. This metric must also be present in the dict
-            reported/returned by the Trainable.
+            reported/returned by the Trainable. If None but a mode was passed,
+            the `ray.tune.result.DEFAULT_METRIC` will be used per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute. Defaults to "max".
         parameter_constraints (list[str]): Parameter constraints, such as
@@ -146,9 +148,13 @@ class AxSearch(Searcher):
         self._live_trial_mapping = {}
 
         if self._ax or self._space:
-            self.setup_experiment()
+            self._setup_experiment()
+
+    def _setup_experiment(self):
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
 
-    def setup_experiment(self):
         if not self._ax:
             self._ax = AxClient()
 
@@ -200,7 +206,8 @@ class AxSearch(Searcher):
             self._metric = metric
         if mode:
             self._mode = mode
-        self.setup_experiment()
+
+        self._setup_experiment()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/bayesopt.py b/python/ray/tune/suggest/bayesopt.py
index af3538058..a3620bb17 100644
--- a/python/ray/tune/suggest/bayesopt.py
+++ b/python/ray/tune/suggest/bayesopt.py
@@ -5,6 +5,7 @@ import json
 from typing import Dict, Optional, Tuple
 
 from ray.tune import ExperimentAnalysis
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Domain, Float, Quantized
 from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
     UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE
@@ -53,7 +54,9 @@ class BayesOptSearch(Searcher):
     Args:
         space (dict): Continuous search space. Parameters will be sampled from
             this space which will be used to run trials.
-        metric (str): The training result objective value attribute.
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         utility_kwargs (dict): Parameters to define the utility function.
@@ -205,9 +208,13 @@ class BayesOptSearch(Searcher):
 
         self.optimizer = None
         if space:
-            self.setup_optimizer()
+            self._setup_optimizer()
+
+    def _setup_optimizer(self):
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
 
-    def setup_optimizer(self):
         self.optimizer = byo.BayesianOptimization(
             f=None,
             pbounds=self._space,
@@ -230,7 +237,7 @@ class BayesOptSearch(Searcher):
         elif self._mode == "min":
             self._metric_op = -1.
 
-        self.setup_optimizer()
+        self._setup_optimizer()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/bohb.py b/python/ray/tune/suggest/bohb.py
index e5f371674..e6e803d30 100644
--- a/python/ray/tune/suggest/bohb.py
+++ b/python/ray/tune/suggest/bohb.py
@@ -6,6 +6,7 @@ import math
 from typing import Dict, Optional, Union
 
 import ConfigSpace
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
     Normal, \
     Quantized, \
@@ -45,7 +46,9 @@ class TuneBOHB(Searcher):
         bohb_config (dict): configuration for HpBandSter BOHB algorithm
         max_concurrent (int): Number of maximum concurrent trials. Defaults
             to 10.
-        metric (str): The training result objective value attribute.
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         seed (int): Optional random seed to initialize the random number
@@ -133,11 +136,15 @@ class TuneBOHB(Searcher):
         super(TuneBOHB, self).__init__(metric=self._metric, mode=mode)
 
         if self._space:
-            self.setup_bohb()
+            self._setup_bohb()
 
-    def setup_bohb(self):
+    def _setup_bohb(self):
         from hpbandster.optimizers.config_generators.bohb import BOHB
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         if self._mode == "max":
             self._metric_op = -1.
         elif self._mode == "min":
@@ -161,7 +168,7 @@ class TuneBOHB(Searcher):
         if mode:
             self._mode = mode
 
-        self.setup_bohb()
+        self._setup_bohb()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/dragonfly.py b/python/ray/tune/suggest/dragonfly.py
index c19f217bb..4be1f47dd 100644
--- a/python/ray/tune/suggest/dragonfly.py
+++ b/python/ray/tune/suggest/dragonfly.py
@@ -7,6 +7,7 @@ import logging
 import pickle
 from typing import Dict, List, Optional, Union
 
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Domain, Float, Quantized
 from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
     UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE
@@ -62,7 +63,9 @@ class DragonflySearch(Searcher):
             an optimizer as the `optimizer` argument. Defines the search space
             and requires a `domain` to be set. Can be automatically converted
             from the `config` dict passed to `tune.run()`.
-        metric (str): The training result objective value attribute.
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         points_to_evaluate (list of lists): A list of points you'd like to run
@@ -177,9 +180,9 @@ class DragonflySearch(Searcher):
             self._opt = optimizer
             self.init_dragonfly()
         elif self._space:
-            self.setup_dragonfly()
+            self._setup_dragonfly()
 
-    def setup_dragonfly(self):
+    def _setup_dragonfly(self):
         """Setup dragonfly when no optimizer has been passed."""
         assert not self._opt, "Optimizer already set."
 
@@ -259,6 +262,10 @@ class DragonflySearch(Searcher):
         elif self._mode == "max":
             self._metric_op = 1.
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
     def set_search_properties(self, metric: Optional[str], mode: Optional[str],
                               config: Dict) -> bool:
         if self._opt:
@@ -270,7 +277,7 @@ class DragonflySearch(Searcher):
         if mode:
             self._mode = mode
 
-        self.setup_dragonfly()
+        self._setup_dragonfly()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/hyperopt.py b/python/ray/tune/suggest/hyperopt.py
index 133dc1da3..3f0b1a939 100644
--- a/python/ray/tune/suggest/hyperopt.py
+++ b/python/ray/tune/suggest/hyperopt.py
@@ -6,6 +6,7 @@ import logging
 from functools import partial
 import pickle
 
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
     Normal, \
     Quantized, \
@@ -50,7 +51,9 @@ class HyperOptSearch(Searcher):
         space (dict): HyperOpt configuration. Parameters will be sampled
             from this configuration and will be used to override
             parameters generated in the variant generation process.
-        metric (str): The training result objective value attribute.
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         points_to_evaluate (list): Initial parameter suggestions to be run
@@ -177,14 +180,22 @@ class HyperOptSearch(Searcher):
                     UNRESOLVED_SEARCH_SPACE.format(
                         par="space", cls=type(self)))
                 space = self.convert_search_space(space)
-            self.domain = hpo.Domain(lambda spc: spc, space)
+            self._space = space
+            self._setup_hyperopt()
+
+    def _setup_hyperopt(self):
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
+        self.domain = hpo.Domain(lambda spc: spc, self._space)
 
     def set_search_properties(self, metric: Optional[str], mode: Optional[str],
                               config: Dict) -> bool:
         if self.domain:
             return False
         space = self.convert_search_space(config)
-        self.domain = hpo.Domain(lambda spc: spc, space)
+        self._space = space
 
         if metric:
             self._metric = metric
@@ -196,6 +207,7 @@ class HyperOptSearch(Searcher):
         elif self._mode == "min":
             self.metric_op = 1.
 
+        self._setup_hyperopt()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/nevergrad.py b/python/ray/tune/suggest/nevergrad.py
index 67cb3db55..d2592dfe9 100644
--- a/python/ray/tune/suggest/nevergrad.py
+++ b/python/ray/tune/suggest/nevergrad.py
@@ -2,6 +2,7 @@ import logging
 import pickle
 from typing import Dict, Optional, Union, List, Sequence
 
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
     Quantized
 from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
@@ -45,7 +46,9 @@ class NevergradSearch(Searcher):
         space (list|nevergrad.parameter.Parameter): Nevergrad parametrization
             to be passed to optimizer on instantiation, or list of parameter
             names if you passed an optimizer object.
-        metric (str): The training result objective value attribute.
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         points_to_evaluate (list): Initial parameter suggestions to be run
@@ -165,9 +168,9 @@ class NevergradSearch(Searcher):
         self.max_concurrent = max_concurrent
 
         if self._nevergrad_opt or self._space:
-            self.setup_nevergrad()
+            self._setup_nevergrad()
 
-    def setup_nevergrad(self):
+    def _setup_nevergrad(self):
         if self._opt_factory:
             self._nevergrad_opt = self._opt_factory(self._space)
 
@@ -177,6 +180,10 @@ class NevergradSearch(Searcher):
         elif self._mode == "min":
             self._metric_op = 1.
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
         if hasattr(self._nevergrad_opt, "instrumentation"):  # added in v0.2.0
             if self._nevergrad_opt.instrumentation.kwargs:
                 if self._nevergrad_opt.instrumentation.args:
@@ -209,7 +216,7 @@ class NevergradSearch(Searcher):
         if mode:
             self._mode = mode
 
-        self.setup_nevergrad()
+        self._setup_nevergrad()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/optuna.py b/python/ray/tune/suggest/optuna.py
index 9c5470135..7076b1f59 100644
--- a/python/ray/tune/suggest/optuna.py
+++ b/python/ray/tune/suggest/optuna.py
@@ -2,7 +2,7 @@ import logging
 import pickle
 from typing import Dict, List, Optional, Tuple, Union
 
-from ray.tune.result import TRAINING_ITERATION
+from ray.tune.result import DEFAULT_METRIC, TRAINING_ITERATION
 from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
     Quantized, Uniform
 from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
@@ -57,8 +57,9 @@ class OptunaSearch(Searcher):
         space (list): Hyperparameter search space definition for Optuna's
             sampler. This is a list, and samples for the parameters will
             be obtained in order.
-        metric (str): Metric that is reported back to Optuna on trial
-            completion.
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         sampler (optuna.samplers.BaseSampler): Optuna sampler used to
@@ -139,9 +140,13 @@ class OptunaSearch(Searcher):
         self._ot_trials = {}
         self._ot_study = None
         if self._space:
-            self.setup_study(mode)
+            self._setup_study(mode)
+
+    def _setup_study(self, mode: str):
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
 
-    def setup_study(self, mode: str):
         self._ot_study = ot.study.create_study(
             storage=self._storage,
             sampler=self._sampler,
@@ -160,7 +165,8 @@ class OptunaSearch(Searcher):
             self._metric = metric
         if mode:
             self._mode = mode
-        self.setup_study(mode)
+
+        self._setup_study(mode)
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/skopt.py b/python/ray/tune/suggest/skopt.py
index 3dee909c6..f8b8d9196 100644
--- a/python/ray/tune/suggest/skopt.py
+++ b/python/ray/tune/suggest/skopt.py
@@ -2,6 +2,7 @@ import logging
 import pickle
 from typing import Dict, List, Optional, Tuple, Union
 
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Categorical, Domain, Float, Integer, Quantized
 from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
     UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE
@@ -75,7 +76,9 @@ class SkOptSearch(Searcher):
             parameters. If you passed an optimizer instance as the
             `optimizer` argument, this should be a list of parameter names
             instead.
-        metric (str): The training result objective value attribute.
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
         points_to_evaluate (list of lists): A list of points you'd like to run
@@ -184,11 +187,11 @@ class SkOptSearch(Searcher):
 
         self._skopt_opt = optimizer
         if self._skopt_opt or self._space:
-            self.setup_skopt()
+            self._setup_skopt()
 
         self._live_trial_mapping = {}
 
-    def setup_skopt(self):
+    def _setup_skopt(self):
         _validate_warmstart(self._parameter_names, self._points_to_evaluate,
                             self._evaluated_rewards)
 
@@ -213,6 +216,10 @@ class SkOptSearch(Searcher):
         elif self._mode == "min":
             self._metric_op = 1.
 
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
+
     def set_search_properties(self, metric: Optional[str], mode: Optional[str],
                               config: Dict) -> bool:
         if self._skopt_opt:
@@ -228,7 +235,7 @@ class SkOptSearch(Searcher):
         if mode:
             self._mode = mode
 
-        self.setup_skopt()
+        self._setup_skopt()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/suggest/zoopt.py b/python/ray/tune/suggest/zoopt.py
index 23177ddaf..966a73f7d 100644
--- a/python/ray/tune/suggest/zoopt.py
+++ b/python/ray/tune/suggest/zoopt.py
@@ -4,6 +4,7 @@ from typing import Dict, Optional, Tuple
 
 import ray
 import ray.cloudpickle as pickle
+from ray.tune.result import DEFAULT_METRIC
 from ray.tune.sample import Categorical, Domain, Float, Integer, Quantized, \
     Uniform
 from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
@@ -113,11 +114,11 @@ class ZOOptSearch(Searcher):
             For discrete dimensions: (discrete, search_range, has_order);
             For grid dimensions: (grid, grid_list).
             More details can be found in zoopt package.
-        metric (str): The training result objective value attribute.
-            Defaults to "episode_reward_mean".
+        metric (str): The training result objective value attribute. If None
+            but a mode was passed, the anonymous metric `_metric` will be used
+            per default.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
-            Defaults to "min".
         parallel_num (int): How many workers to parallel. Note that initial
             phase may start less workers than this number. More details can
             be found in zoopt package.
@@ -171,9 +172,13 @@ class ZOOptSearch(Searcher):
         super(ZOOptSearch, self).__init__(metric=self._metric, mode=mode)
 
         if self._dim_dict:
-            self.setup_zoopt()
+            self._setup_zoopt()
+
+    def _setup_zoopt(self):
+        if self._metric is None and self._mode:
+            # If only a mode was passed, use anonymous metric
+            self._metric = DEFAULT_METRIC
 
-    def setup_zoopt(self):
         _dim_list = []
         for k in self._dim_dict:
             self._dim_keys.append(k)
@@ -203,7 +208,7 @@ class ZOOptSearch(Searcher):
         elif self._mode == "min":
             self._metric_op = 1.
 
-        self.setup_zoopt()
+        self._setup_zoopt()
         return True
 
     def suggest(self, trial_id: str) -> Optional[Dict]:
diff --git a/python/ray/tune/tests/test_function_api.py b/python/ray/tune/tests/test_function_api.py
index 4bb27ebd9..305e8abb0 100644
--- a/python/ray/tune/tests/test_function_api.py
+++ b/python/ray/tune/tests/test_function_api.py
@@ -12,7 +12,7 @@ from ray.tune.logger import NoopLogger
 from ray.tune.utils.trainable import TrainableUtil
 from ray.tune.function_runner import with_parameters, wrap_function, \
     FuncCheckpointUtil
-from ray.tune.result import TRAINING_ITERATION
+from ray.tune.result import DEFAULT_METRIC, TRAINING_ITERATION
 
 
 def creator_generator(logdir):
@@ -468,7 +468,7 @@ class FunctionApiTest(unittest.TestCase):
         self.assertEquals(trial_2.last_result["metric"], 500_000)
         self.assertEquals(trial_2.last_result["cp"], "DIR")
 
-    def test_return_anonymous(self):
+    def testReturnAnonymous(self):
         def train(config):
             return config["a"]
 
@@ -477,10 +477,10 @@ class FunctionApiTest(unittest.TestCase):
                 "a": tune.grid_search([4, 8])
             }).trials
 
-        self.assertEquals(trial_1.last_result["_metric"], 4)
-        self.assertEquals(trial_2.last_result["_metric"], 8)
+        self.assertEquals(trial_1.last_result[DEFAULT_METRIC], 4)
+        self.assertEquals(trial_2.last_result[DEFAULT_METRIC], 8)
 
-    def test_return_specific(self):
+    def testReturnSpecific(self):
         def train(config):
             return {"m": config["a"]}
 
@@ -492,7 +492,7 @@ class FunctionApiTest(unittest.TestCase):
         self.assertEquals(trial_1.last_result["m"], 4)
         self.assertEquals(trial_2.last_result["m"], 8)
 
-    def test_yield_anonymous(self):
+    def testYieldAnonymous(self):
         def train(config):
             for i in range(10):
                 yield config["a"] + i
@@ -502,10 +502,10 @@ class FunctionApiTest(unittest.TestCase):
                 "a": tune.grid_search([4, 8])
             }).trials
 
-        self.assertEquals(trial_1.last_result["_metric"], 4 + 9)
-        self.assertEquals(trial_2.last_result["_metric"], 8 + 9)
+        self.assertEquals(trial_1.last_result[DEFAULT_METRIC], 4 + 9)
+        self.assertEquals(trial_2.last_result[DEFAULT_METRIC], 8 + 9)
 
-    def test_yield_specific(self):
+    def testYieldSpecific(self):
         def train(config):
             for i in range(10):
                 yield {"m": config["a"] + i}
diff --git a/python/ray/tune/tests/test_searchers.py b/python/ray/tune/tests/test_searchers.py
index 102111573..0b50be49d 100644
--- a/python/ray/tune/tests/test_searchers.py
+++ b/python/ray/tune/tests/test_searchers.py
@@ -21,6 +21,12 @@ def _invalid_objective(config):
 
 
 class InvalidValuesTest(unittest.TestCase):
+    """
+    Test searcher handling of invalid values (NaN, -inf, inf).
+    Implicitly tests automatic config conversion and default (anonymous)
+    mode handling.
+    """
+
     def setUp(self):
         self.config = {"report": tune.uniform(0.0, 5.0)}
 
@@ -65,7 +71,6 @@ class InvalidValuesTest(unittest.TestCase):
             # At least one nan, inf, -inf and float
             search_alg=BayesOptSearch(random_state=1234),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=8,
             reuse_actors=False)
@@ -80,7 +85,6 @@ class InvalidValuesTest(unittest.TestCase):
             _invalid_objective,
             search_alg=TuneBOHB(seed=1000),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=8,
             reuse_actors=False)
@@ -97,7 +101,6 @@ class InvalidValuesTest(unittest.TestCase):
             _invalid_objective,
             search_alg=DragonflySearch(domain="euclidean", optimizer="random"),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=8,
             reuse_actors=False)
@@ -113,7 +116,6 @@ class InvalidValuesTest(unittest.TestCase):
             # At least one nan, inf, -inf and float
             search_alg=HyperOptSearch(random_state_seed=1234),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=8,
             reuse_actors=False)
@@ -131,7 +133,6 @@ class InvalidValuesTest(unittest.TestCase):
             _invalid_objective,
             search_alg=NevergradSearch(optimizer=ng.optimizers.RandomSearch),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=16,
             reuse_actors=False)
@@ -149,7 +150,6 @@ class InvalidValuesTest(unittest.TestCase):
             _invalid_objective,
             search_alg=OptunaSearch(sampler=RandomSampler(seed=1234)),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=8,
             reuse_actors=False)
@@ -166,7 +166,6 @@ class InvalidValuesTest(unittest.TestCase):
             _invalid_objective,
             search_alg=SkOptSearch(),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=8,
             reuse_actors=False)
@@ -183,7 +182,6 @@ class InvalidValuesTest(unittest.TestCase):
             _invalid_objective,
             search_alg=ZOOptSearch(budget=100, parallel_num=4),
             config=self.config,
-            metric="_metric",
             mode="max",
             num_samples=8,
             reuse_actors=False)
diff --git a/python/ray/tune/tests/test_trial_scheduler.py b/python/ray/tune/tests/test_trial_scheduler.py
index a2cd02fb2..30724ed46 100644
--- a/python/ray/tune/tests/test_trial_scheduler.py
+++ b/python/ray/tune/tests/test_trial_scheduler.py
@@ -2065,6 +2065,36 @@ class AsyncHyperBandSuite(unittest.TestCase):
 
         self._test_metrics(result2, "mean_loss", "min")
 
+    def _testAnonymousMetricEndToEnd(self, scheduler_cls, searcher=None):
+        def train(config):
+            return config["value"]
+
+        out = tune.run(
+            train,
+            mode="max",
+            num_samples=1,
+            config={"value": tune.uniform(-2., 2.)},
+            scheduler=scheduler_cls(),
+            search_alg=searcher)
+
+        self.assertTrue(bool(out.best_trial))
+
+    def testAnonymousMetricEndToEndFIFO(self):
+        self._testAnonymousMetricEndToEnd(FIFOScheduler)
+
+    def testAnonymousMetricEndToEndASHA(self):
+        self._testAnonymousMetricEndToEnd(AsyncHyperBandScheduler)
+
+    def testAnonymousMetricEndToEndBOHB(self):
+        from ray.tune.suggest.bohb import TuneBOHB
+        self._testAnonymousMetricEndToEnd(HyperBandForBOHB, TuneBOHB())
+
+    def testAnonymousMetricEndToEndMedian(self):
+        self._testAnonymousMetricEndToEnd(MedianStoppingRule)
+
+    def testAnonymousMetricEndToEndPBT(self):
+        self._testAnonymousMetricEndToEnd(PopulationBasedTraining)
+
 
 if __name__ == "__main__":
     import pytest
diff --git a/python/ray/tune/trial_runner.py b/python/ray/tune/trial_runner.py
index 8fab9450c..800d30304 100644
--- a/python/ray/tune/trial_runner.py
+++ b/python/ray/tune/trial_runner.py
@@ -12,8 +12,8 @@ from ray.tune import TuneError
 from ray.tune.callback import CallbackList
 from ray.tune.stopper import NoopStopper
 from ray.tune.ray_trial_executor import RayTrialExecutor
-from ray.tune.result import (TIME_THIS_ITER_S, RESULT_DUPLICATE,
-                             SHOULD_CHECKPOINT)
+from ray.tune.result import (DEFAULT_METRIC, TIME_THIS_ITER_S,
+                             RESULT_DUPLICATE, SHOULD_CHECKPOINT)
 from ray.tune.syncer import get_cloud_syncer
 from ray.tune.trial import Checkpoint, Trial
 from ray.tune.schedulers import FIFOScheduler, TrialScheduler
@@ -614,13 +614,19 @@ class TrialRunner:
         in the last result. If the only item is `done=True`, this
         means that no result was ever received and the trial just
         returned. This is also okay and will not raise an error.
+
+        This will ignore checking for the DEFAULT_METRIC.
         """
         if int(os.environ.get("TUNE_DISABLE_STRICT_METRIC_CHECKING",
                               0)) != 1 and (len(result) > 1
                                             or "done" not in result):
-            base_metric = self._metric
-            scheduler_metric = self._scheduler_alg.metric
-            search_metrics = self._search_alg.metric
+            base_metric = self._metric \
+                if self._metric != DEFAULT_METRIC else None
+            scheduler_metric = self._scheduler_alg.metric \
+                if self._scheduler_alg.metric != DEFAULT_METRIC else None
+            search_metrics = self._search_alg.metric \
+                if self._search_alg.metric != DEFAULT_METRIC else None
+
             if isinstance(search_metrics, str):
                 search_metrics = [search_metrics]