diff --git a/nncf/openvino/graph/metatypes/groups.py b/nncf/openvino/graph/metatypes/groups.py
index eed716f9f32..38ffff12753 100644
--- a/nncf/openvino/graph/metatypes/groups.py
+++ b/nncf/openvino/graph/metatypes/groups.py
@@ -183,12 +183,13 @@
 
 
 # Contains the operation metatypes for which bias can be applied.
-OPERATIONS_WITH_BIAS = [
+# Limited operations scope
+OPERATIONS_WITH_BIAS_REDUCED = [
     ov_metatypes.OVConvolutionMetatype,
-    # TODO: add all metatypes with bias
     ov_metatypes.OVMatMulMetatype,
 ]
 
+OPERATIONS_WITH_BIAS = [*OPERATIONS_WITH_BIAS_REDUCED, ov_metatypes.OVDepthwiseConvolutionMetatype]
 
 CONV_OPERATIONS = [
     ov_metatypes.OVConvolutionMetatype,
diff --git a/nncf/openvino/graph/node_utils.py b/nncf/openvino/graph/node_utils.py
index 59d5e58c45b..ae9bf264ea5 100644
--- a/nncf/openvino/graph/node_utils.py
+++ b/nncf/openvino/graph/node_utils.py
@@ -44,17 +44,23 @@
 InplaceInsertionFnType = Callable[[ov.Node, int], ov.Node]
 
 
-def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool:
+def is_node_with_bias(
+    node: NNCFNode, nncf_graph: NNCFGraph, metatypes_with_bias: Optional[List[OVOpMetatype]] = None
+) -> bool:
     """
     Checks if the node has a bias or not.
 
     :param node: The node to check.
     :param nncf_graph: NNCFGraph instance.
+    :param metatypes_with_bias: List of the metatypes that contains biases.
     :return: Return `True` if `node` corresponds to the operation
         with bias (bias is added to the output tensor of that operation),
         `False` otherwise.
     """
-    if node.metatype not in OPERATIONS_WITH_BIAS:
+    if metatypes_with_bias is None:
+        metatypes_with_bias = OPERATIONS_WITH_BIAS
+
+    if node.metatype not in metatypes_with_bias:
         return False
 
     add_node = nncf_graph.get_next_nodes(node)[0]
diff --git a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py
index 58cc3f04ff2..159b2111f1a 100644
--- a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py
+++ b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py
@@ -20,6 +20,7 @@
 from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
 from nncf.experimental.tensor import Tensor
 from nncf.openvino.graph.metatypes.groups import FAKE_QUANTIZE_OPERATIONS
+from nncf.openvino.graph.metatypes.groups import OPERATIONS_WITH_BIAS_REDUCED
 from nncf.openvino.graph.node_utils import get_bias_value
 from nncf.openvino.graph.node_utils import is_node_with_bias
 from nncf.openvino.graph.transformations.command_creation import OVCommandCreator
@@ -95,7 +96,7 @@ def process_model_output(raw_data: Dict, output_name: str) -> Tensor:
 
     @staticmethod
     def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool:
-        return is_node_with_bias(node, nncf_graph)
+        return is_node_with_bias(node, nncf_graph, OPERATIONS_WITH_BIAS_REDUCED)
 
     @staticmethod
     def get_node_names_for_input_output_statistics(node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[str, str]:
diff --git a/tests/post_training/data/ptq_reference_data.yaml b/tests/post_training/data/ptq_reference_data.yaml
index 3605403a440..6792bbe809e 100644
--- a/tests/post_training/data/ptq_reference_data.yaml
+++ b/tests/post_training/data/ptq_reference_data.yaml
@@ -146,6 +146,12 @@ timm/mobilenetv3_small_050_backend_OV:
   metric_value: 0.42184
 timm/mobilenetv3_small_050_backend_TORCH:
   metric_value: 0.4291
+timm/mobilenetv3_small_050_BC_backend_FP32:
+  metric_value: 0.57906
+timm/mobilenetv3_small_050_BC_backend_ONNX:
+  metric_value: 0.56496
+timm/mobilenetv3_small_050_BC_backend_OV:
+  metric_value: 0.56476
 timm/regnetx_002_backend_CUDA_TORCH:
   metric_value: 0.67452
 timm/regnetx_002_backend_FP32:
diff --git a/tests/post_training/model_scope.py b/tests/post_training/model_scope.py
index e643b68087d..69c1b9890e0 100644
--- a/tests/post_training/model_scope.py
+++ b/tests/post_training/model_scope.py
@@ -188,6 +188,16 @@
         },
         "backends": ALL_PTQ_BACKENDS,
     },
+    {
+        "reported_name": "timm/mobilenetv3_small_050_BC",
+        "model_id": "mobilenetv3_small_050",
+        "pipeline_cls": ImageClassificationTimm,
+        "compression_params": {
+            "preset": QuantizationPreset.MIXED,
+            "fast_bias_correction": False,
+        },
+        "backends": [BackendType.ONNX, BackendType.OV],
+    },
     {
         "reported_name": "timm/regnetx_002",
         "model_id": "regnetx_002",