diff --git a/golem/core/tuning/hyperopt_tuner.py b/golem/core/tuning/hyperopt_tuner.py
index e954aee4..cf6471a6 100644
--- a/golem/core/tuning/hyperopt_tuner.py
+++ b/golem/core/tuning/hyperopt_tuner.py
@@ -1,11 +1,12 @@
 from abc import ABC
 from datetime import timedelta
-from typing import Optional, Callable, Dict
+from typing import Callable, Dict, Optional
 
 import numpy as np
-from hyperopt import tpe, hp
+from hyperopt import hp, tpe
 from hyperopt.early_stop import no_progress_loss
-from hyperopt.pyll import Apply
+from hyperopt.pyll import Apply, scope
+from hyperopt.pyll_utils import validate_label
 
 from golem.core.adapter import BaseOptimizationAdapter
 from golem.core.log import default_log
@@ -14,6 +15,14 @@
 from golem.core.tuning.tuner_interface import BaseTuner
 
 
+@validate_label
+def hp_randint(label, *args, **kwargs):
+    return scope.int(scope.hyperopt_param(label, scope.randint(*args, **kwargs)))
+
+
+hp.randint = hp_randint
+
+
 class HyperoptTuner(BaseTuner, ABC):
     """Base class for hyperparameters optimization based on hyperopt library
 
diff --git a/test/unit/tuning/test_tuning.py b/test/unit/tuning/test_tuning.py
index 08addcd4..e8f4bc87 100644
--- a/test/unit/tuning/test_tuning.py
+++ b/test/unit/tuning/test_tuning.py
@@ -9,9 +9,9 @@
 from golem.core.tuning.search_space import SearchSpace
 from golem.core.tuning.sequential import SequentialTuner
 from golem.core.tuning.simultaneous import SimultaneousTuner
-from test.unit.mocks.common_mocks import MockAdapter, MockObjectiveEvaluate, mock_graph_with_params, \
-    opt_graph_with_params, MockNode, MockDomainStructure
-from test.unit.utils import ParamsSumMetric, ParamsProductMetric
+from test.unit.mocks.common_mocks import (MockAdapter, MockDomainStructure, MockNode, MockObjectiveEvaluate,
+                                          mock_graph_with_params, opt_graph_with_params)
+from test.unit.utils import ParamsProductMetric, ParamsSumMetric
 
 
 def not_tunable_mock_graph():
@@ -40,8 +40,7 @@ def search_space():
                 'hyperopt-dist': hp.choice,
                 'sampling-scope': [['A', 'B', 'C']],
                 'type': 'categorical'
-            }
-        },
+            }},
         'b': {
             'b1': {
                 'hyperopt-dist': hp.choice,
@@ -53,6 +52,11 @@ def search_space():
                 'sampling-scope': [0.05, 1.0],
                 'type': 'continuous'
             },
+            'b3': {
+                'hyperopt-dist': hp.randint,
+                'sampling-scope': [1, 1000],
+                'type': 'discrete'
+            }
         },
         'e': {
             'e1': {
@@ -133,3 +137,16 @@ def test_multi_objective_tuning(search_space, tuner_cls, init_graph, adapter, ob
         final_metric = obj_eval.evaluate(graph)
         assert final_metric is not None
         assert not init_metric.dominates(final_metric)
+
+
+@pytest.mark.parametrize('tuner_cls', [SequentialTuner, SimultaneousTuner])
+def test_hyperopt_returns_native_types(search_space, tuner_cls):
+    obj_eval = MockObjectiveEvaluate(Objective({'sum_metric': ParamsSumMetric.get_value}))
+    adapter = MockAdapter()
+    graph = opt_graph_with_params()
+    tuner = tuner_cls(obj_eval, search_space, adapter, iterations=20)
+    tuned_graph = tuner.tune(deepcopy(graph))
+    for node in tuned_graph.nodes:
+        for param, val in node.parameters.items():
+            assert val.__class__.__module__ != 'numpy', (f'The parameter "{param}" should not be a numpy type. '
+                                                         f'Got "{type(val)}".')