diff --git a/environment-deprecated.yml b/environment-deprecated.yml
index 24119a2..d8f685a 100644
--- a/environment-deprecated.yml
+++ b/environment-deprecated.yml
@@ -4,7 +4,7 @@ channels:
   - conda-forge
   - nodefaults
 dependencies:
-  - lightgbm
+  - lightgbm <4.0
   - numpy
   - python>=3.8
   - pre-commit
diff --git a/pixi.toml b/pixi.toml
index 03324a4..74e0cff 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -1,6 +1,6 @@
 [project] # TODO: move to pyproject.toml once pixi supports it
 name = "slim-trees"
-version = "0.2.1"
+version = "0.2.2"
 description = "A python package for efficient pickling of ML models."
 authors = ["Pavel Zwerschke <pavelzw@gmail.com>"]
 channels = ["conda-forge"]
@@ -13,7 +13,7 @@ lint = "pre-commit run --all"
 [dependencies]
 python = ">=3.8"
 pip = "*"
-lightgbm = "*"
+lightgbm = "<4.0"
 numpy = "*"
 pre-commit = "*"
 pandas = "*"
diff --git a/pyproject.toml b/pyproject.toml
index e591655..eac76b8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "slim-trees"
 description = "A python package for efficient pickling of ML models."
-version = "0.2.1"
+version = "0.2.2"
 readme = "README.md"
 license = "MIT"
 requires-python = ">=3.8"
@@ -25,7 +25,7 @@ dependencies = [
 
 [project.optional-dependencies]
 lightgbm = [
-    "lightgbm",
+    "lightgbm <4.0",
 ]
 scikit-learn = [
     "scikit-learn <1.3.0",
diff --git a/slim_trees/pickling.py b/slim_trees/pickling.py
index dfa3e91..f3af990 100644
--- a/slim_trees/pickling.py
+++ b/slim_trees/pickling.py
@@ -13,6 +13,10 @@ def __init__(self):
         self.open = open
         self.compress = lambda data: data
 
+    @staticmethod
+    def decompress(data):
+        return data
+
 
 def _get_compression_from_path(path: Union[str, pathlib.Path]) -> str:
     compressions = {
@@ -120,7 +124,9 @@ def dumps_compressed(
 
 
 def load_compressed(
-    path: Union[str, pathlib.Path], compression: Optional[Union[str, dict]] = None
+    path: Union[str, pathlib.Path],
+    compression: Optional[Union[str, dict]] = None,
+    unpickler_class: type = pickle.Unpickler,
 ) -> Any:
     """
     Loads a compressed model.
@@ -129,15 +135,22 @@ def load_compressed(
                         set to the compression method and other key-value pairs which are forwarded
                         to open() of the compression library.
                         Inspired by the pandas.to_csv interface.
+    :param unpickler_class: custom unpickler class derived from pickle.Unpickler.
+                            This is useful to restrict possible imports or to allow unpickling
+                            when required module or function names have been refactored.
     """
     compression_method, kwargs = _unpack_compression_args(compression, path)
     with _get_compression_library(compression_method).open(
         path, mode="rb", **kwargs
     ) as file:
-        return pickle.load(file)
+        return unpickler_class(file).load()
 
 
-def loads_compressed(data: bytes, compression: Optional[Union[str, dict]] = None):
+def loads_compressed(
+    data: bytes,
+    compression: Optional[Union[str, dict]] = None,
+    unpickler_class: type = pickle.Unpickler,
+) -> Any:
     """
     Loads a compressed model.
     :param data: bytes containing the pickled object.
@@ -145,13 +158,16 @@ def loads_compressed(data: bytes, compression: Optional[Union[str, dict]] = None
                         set to the compression method and other key-value pairs which are forwarded
                         to open() of the compression library. Defaults to 'no' compression.
                         Inspired by the pandas.to_csv interface.
+    :param unpickler_class: custom unpickler class derived from pickle.Unpickler.
+                            This is useful to restrict possible imports or to allow unpickling
+                            when required module or function names have been refactored.
     """
     if compression is None:
         compression = "no"
 
     compression_method, kwargs = _unpack_compression_args(compression, None)
     data_uncompressed = _get_compression_library(compression_method).decompress(data)
-    return pickle.loads(data_uncompressed)
+    return unpickler_class(io.BytesIO(data_uncompressed)).load()
 
 
 def get_pickled_size(
diff --git a/tests/test_lgbm_compression.py b/tests/test_lgbm_compression.py
index e2d4f44..eaaf5ec 100644
--- a/tests/test_lgbm_compression.py
+++ b/tests/test_lgbm_compression.py
@@ -1,4 +1,5 @@
 import os
+import pickle
 
 import numpy as np
 import pytest
@@ -10,9 +11,9 @@
     get_load_times,
 )
 
-from slim_trees import dump_lgbm_compressed
+from slim_trees import dump_lgbm_compressed, dumps_lgbm_compressed
 from slim_trees.lgbm_booster import _booster_pickle
-from slim_trees.pickling import dump_compressed, load_compressed
+from slim_trees.pickling import dump_compressed, load_compressed, loads_compressed
 
 
 @pytest.fixture
@@ -84,4 +85,24 @@ def test_tree_version_unpickle(diabetes_toy_df, lgbm_regressor):
     assert_version_unpickle(_booster_pickle, lgbm_regressor.booster_)
 
 
+class _TestUnpickler(pickle.Unpickler):
+    def find_class(self, module, name):
+        if module.startswith("lightgbm"):
+            raise ImportError(f"Module '{module}' not allowed in this test")
+        return super().find_class(module, name)
+
+
+def test_load_compressed_custom_unpickler(tmp_path, lgbm_regressor):
+    model_path = tmp_path / "model_compressed.pickle.lzma"
+    dump_lgbm_compressed(lgbm_regressor, model_path)
+    with pytest.raises(ImportError, match="lightgbm.*not allowed"):
+        load_compressed(model_path, unpickler_class=_TestUnpickler)
+
+
+def test_loads_compressed_custom_unpickler(lgbm_regressor):
+    compressed = dumps_lgbm_compressed(lgbm_regressor)
+    with pytest.raises(ImportError, match="lightgbm.*not allowed"):
+        loads_compressed(compressed, unpickler_class=_TestUnpickler)
+
+
 # todo add tests for large models
diff --git a/tests/test_sklearn_compression.py b/tests/test_sklearn_compression.py
index c3fce04..7b1d0b7 100644
--- a/tests/test_sklearn_compression.py
+++ b/tests/test_sklearn_compression.py
@@ -1,4 +1,5 @@
 import os
+import pickle
 
 import numpy as np
 import pytest
@@ -11,8 +12,8 @@
     get_load_times,
 )
 
-from slim_trees import dump_sklearn_compressed
-from slim_trees.pickling import dump_compressed, load_compressed
+from slim_trees import dump_sklearn_compressed, dumps_sklearn_compressed
+from slim_trees.pickling import dump_compressed, load_compressed, loads_compressed
 from slim_trees.sklearn_tree import _tree_pickle
 
 
@@ -126,4 +127,24 @@ def test_tree_version_unpickle(diabetes_toy_df, decision_tree_regressor):
     assert_version_unpickle(_tree_pickle, decision_tree_regressor.tree_)
 
 
+class _TestUnpickler(pickle.Unpickler):
+    def find_class(self, module, name):
+        if module.startswith("sklearn"):
+            raise ImportError(f"Module '{module}' not allowed in this test")
+        return super().find_class(module, name)
+
+
+def test_load_compressed_custom_unpickler(tmp_path, random_forest_regressor):
+    model_path = tmp_path / "model_compressed.pickle.lzma"
+    dump_sklearn_compressed(random_forest_regressor, model_path)
+    with pytest.raises(ImportError, match="sklearn.*not allowed"):
+        load_compressed(model_path, unpickler_class=_TestUnpickler)
+
+
+def test_loads_compressed_custom_unpickler(random_forest_regressor):
+    compressed = dumps_sklearn_compressed(random_forest_regressor)
+    with pytest.raises(ImportError, match="sklearn.*not allowed"):
+        loads_compressed(compressed, unpickler_class=_TestUnpickler)
+
+
 # todo add tests for large models