diff --git a/pyproject.toml b/pyproject.toml
index c6d98c36..a7b16e6c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,6 +62,7 @@ test = [
 ]
 all_extras = [
   "hyperactive[integrations]",
+  "optuna<5",
 ]
 
 
diff --git a/src/hyperactive/opt/una/__init__.py b/src/hyperactive/opt/una/__init__.py
new file mode 100644
index 00000000..58de4fd7
--- /dev/null
+++ b/src/hyperactive/opt/una/__init__.py
@@ -0,0 +1,6 @@
+"""Grid search with sklearn style grid and backends."""
+# copyright: hyperactive developers, MIT License (see LICENSE file)
+
+from hyperactive.opt.una._optuna import OptunaOptimizer
+
+__all__ = ["OptunaOptimizer"]
diff --git a/src/hyperactive/opt/una/_optuna.py b/src/hyperactive/opt/una/_optuna.py
new file mode 100644
index 00000000..1c210ff5
--- /dev/null
+++ b/src/hyperactive/opt/una/_optuna.py
@@ -0,0 +1,104 @@
+"""Optuna optimizer interface."""
+# copyright: hyperactive developers, MIT License (see LICENSE file)
+
+from hyperactive.base import BaseOptimizer
+
+
+class OptunaOptimizer(BaseOptimizer):
+    """Optuna optimizer interface.
+
+    Parameters
+    ----------
+    param_space : dict[str, tuple or list or optuna distributions]
+        The search space to explore. Dictionary with parameter names
+        as keys and either tuples/lists of (low, high) or
+        optuna distribution objects as values.
+    n_trials : int, default=100
+        Number of optimization trials.
+    experiment : BaseExperiment, optional
+        The experiment to optimize parameters for.
+        Optional, can be passed later via ``set_params``.
+
+    Example
+    -------
+    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
+    >>> from hyperactive.opt.una import OptunaOptimizer
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.svm import SVC
+    >>> X, y = load_iris(return_X_y=True)
+    >>> sklearn_exp = SklearnCvExperiment(estimator=SVC(), X=X, y=y)
+    >>> param_space = {
+    ...     "C": (0.01, 10),
+    ...     "gamma": (0.0001, 10),
+    ... }
+    >>> optimizer = OptunaOptimizer(
+    ...     param_space=param_space, n_trials=50, experiment=sklearn_exp
+    ... )
+    >>> best_params = optimizer.run()
+    """
+
+    _tags = {
+        "python_dependencies": ["optuna"],
+        "info:name": "Optuna-based optimizer",
+    }
+
+    def __init__(
+        self,
+        param_space=None,
+        n_trials=100,
+        experiment=None
+    ):
+        self.param_space = param_space
+        self.n_trials = n_trials
+        self.experiment = experiment
+        super().__init__()
+
+    def _objective(self, trial):
+        params = {}
+        for key, space in self.param_space.items():
+            if hasattr(space, "suggest"):  # optuna distribution object
+                params[key] = trial._suggest(space, key)
+            elif isinstance(space, (tuple, list)) and len(space) == 2:
+                low, high = space
+                # Decide type based on low/high type
+                if isinstance(low, int) and isinstance(high, int):
+                    params[key] = trial.suggest_int(key, low, high)
+                else:
+                    params[key] = trial.suggest_float(key, low, high, log=False)
+            else:
+                raise ValueError(f"Invalid parameter space for key '{key}': {space}")
+
+        # Evaluate experiment with suggested params
+        return self.experiment(**params)
+
+    def _run(self, experiment, param_space, n_trials):
+        import optuna
+
+        study = optuna.create_study(direction="minimize")
+        study.optimize(self._objective, n_trials=n_trials)
+
+        self.best_score_ = study.best_value
+        self.best_params_ = study.best_params
+        return study.best_params
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the optimizer."""
+
+        from hyperactive.experiment.integrations import SklearnCvExperiment
+        from sklearn.datasets import load_iris
+        from sklearn.svm import SVC
+
+        X, y = load_iris(return_X_y=True)
+        sklearn_exp = SklearnCvExperiment(estimator=SVC(), X=X, y=y)
+
+        param_space = {
+            "C": (0.01, 10),
+            "gamma": (0.0001, 10),
+        }
+
+        return [{
+            "param_space": param_space,
+            "n_trials": 10,
+            "experiment": sklearn_exp,
+        }]
diff --git a/src/hyperactive/tests/test_all_objects.py b/src/hyperactive/tests/test_all_objects.py
index b5b4955f..9a14f372 100644
--- a/src/hyperactive/tests/test_all_objects.py
+++ b/src/hyperactive/tests/test_all_objects.py
@@ -6,6 +6,7 @@
 from skbase.testing import BaseFixtureGenerator as _BaseFixtureGenerator
 from skbase.testing import QuickTester as _QuickTester
 from skbase.testing import TestAllObjects as _TestAllObjects
+from skbase.utils.dependencies import _check_estimator_deps
 
 from hyperactive._registry import all_objects
 from hyperactive.tests._config import EXCLUDE_ESTIMATORS, EXCLUDED_TESTS
@@ -120,11 +121,13 @@ def _all_objects(self):
         if isclass(filter):
             obj_list = [obj for obj in obj_list if issubclass(obj, filter)]
 
+        def run_test_for_class(obj):
+            return _check_estimator_deps(obj, severity="none")
+
         # run_test_for_class selects the estimators to run
-        # based on whether they have changed, and whether they have all dependencies
-        # internally, uses the ONLY_CHANGED_MODULES flag,
+        # based on whether they have all dependencies
         # and checks the python env against python_dependencies tag
-        # obj_list = [obj for obj in obj_list if run_test_for_class(obj)]
+        obj_list = [obj for obj in obj_list if run_test_for_class(obj)]
 
         return obj_list