diff --git a/tpot2/config/get_configspace.py b/tpot2/config/get_configspace.py
index 9b03718b..6e2fb733 100644
--- a/tpot2/config/get_configspace.py
+++ b/tpot2/config/get_configspace.py
@@ -436,6 +436,22 @@ def get_configspace(name, n_classes=3, n_samples=1000, n_features=100, random_st
     #raise error
     raise ValueError(f"Could not find configspace for {name}")
    
+def flatten_group_names(name):
+    #if string 
+    if isinstance(name, str):
+        if name in GROUPNAMES:
+            return flatten_group_names(GROUPNAMES[name])
+        else:
+            return name
+    
+    flattened_list = []
+    for key in name:
+        if key in GROUPNAMES:
+            flattened_list.extend(flatten_group_names(GROUPNAMES[key]))
+        else:
+            flattened_list.append(key)
+    
+    return flattened_list
 
 def get_search_space(name, n_classes=3, n_samples=1000, n_features=100, random_state=None, return_choice_pipeline=True, base_node=EstimatorNode):
     """
@@ -471,6 +487,7 @@ def get_search_space(name, n_classes=3, n_samples=1000, n_features=100, random_s
         Note: for some special cases with methods using wrapped estimators, the returned search space is a TPOT2.search_spaces.pipelines.WrapperPipeline object.
         
     """
+    name = flatten_group_names(name)
 
     #if list of names, return a list of EstimatorNodes
     if isinstance(name, list) or isinstance(name, np.ndarray):
@@ -483,9 +500,9 @@ def get_search_space(name, n_classes=3, n_samples=1000, n_features=100, random_s
         else:
             return np.hstack(search_spaces)
     
-    if name in GROUPNAMES:
-        name_list = GROUPNAMES[name]
-        return get_search_space(name_list, n_classes=n_classes, n_samples=n_samples, n_features=n_features, random_state=random_state, return_choice_pipeline=return_choice_pipeline, base_node=base_node)
+    # if name in GROUPNAMES:
+    #     name_list = GROUPNAMES[name]
+    #     return get_search_space(name_list, n_classes=n_classes, n_samples=n_samples, n_features=n_features, random_state=random_state, return_choice_pipeline=return_choice_pipeline, base_node=base_node)
     
     return get_node(name, n_classes=n_classes, n_samples=n_samples, n_features=n_features, random_state=random_state, base_node=base_node)