diff --git a/configs/hparams_search/mnist_optuna.yaml b/configs/hparams_search/mnist_optuna.yaml
index 8edecd810..cd8e12937 100644
--- a/configs/hparams_search/mnist_optuna.yaml
+++ b/configs/hparams_search/mnist_optuna.yaml
@@ -1,7 +1,7 @@
 # @package _global_
 
 # example hyperparameter optimization of some experiment with Optuna:
-# python train.py -m hparams_search=mnist_optuna experiment=example_simple hydra.sweeper.n_trials=30
+# python train.py -m hparams_search=mnist_optuna experiment=example
 
 defaults:
   - override /hydra/sweeper: optuna
@@ -10,35 +10,35 @@ defaults:
 # make sure this is the correct name of some metric logged in lightning module!
 optimized_metric: "val/acc_best"
 
+# here we define Optuna hyperparameter search
+# it optimizes for value returned from function with @hydra.main decorator
+# docs: https://hydra.cc/docs/next/plugins/optuna_sweeper
 hydra:
-  # here we define Optuna hyperparameter search
-  # it optimizes for value returned from function with @hydra.main decorator
-  # learn more here: https://hydra.cc/docs/next/plugins/optuna_sweeper
   sweeper:
     _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
+
+    # storage URL to persist optimization results
+    # for example, you can use SQLite if you set 'sqlite:///example.db'
     storage: null
+
+    # name of the study to persist optimization results
     study_name: null
+
+    # number of parallel workers
     n_jobs: 1
 
     # 'minimize' or 'maximize' the objective
     direction: maximize
 
-    # number of experiments that will be executed
-    n_trials: 20
+    # total number of runs that will be executed
+    n_trials: 25
 
     # choose Optuna hyperparameter sampler
-    # learn more here: https://optuna.readthedocs.io/en/stable/reference/samplers.html
+    # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html
     sampler:
       _target_: optuna.samplers.TPESampler
       seed: 12345
-      consider_prior: true
-      prior_weight: 1.0
-      consider_magic_clip: true
-      consider_endpoints: false
-      n_startup_trials: 10
-      n_ei_candidates: 24
-      multivariate: false
-      warn_independent_sampling: true
+      n_startup_trials: 10 # number of random sampling runs before optimization starts
 
     # define range of hyperparameters
     search_space: