diff --git a/src/deep_neurographs/intake.py b/src/deep_neurographs/intake.py
index a4286e4..b304bfe 100644
--- a/src/deep_neurographs/intake.py
+++ b/src/deep_neurographs/intake.py
@@ -81,7 +81,6 @@ def build_neurograph_from_gcs_zips(
     img_path=None,
     min_size=MIN_SIZE,
     n_proposals_per_leaf=N_PROPOSALS_PER_LEAF,
-    search_radius=SEARCH_RADIUS,
     prune=PRUNE,
     prune_depth=PRUNE_DEPTH,
     optimize_proposals=OPTIMIZE_PROPOSALS,
@@ -106,9 +105,6 @@ def build_neurograph_from_gcs_zips(
     n_proposals_per_leaf : int, optional
         Number of edge proposals generated from each leaf node in an swc file.
         The default is the global variable "N_PROPOSALS_PER_LEAF".
-    search_radius : float, optional
-        Maximum Euclidean length of an edge proposal. The default is the
-        global variable "SEARCH_RADIUS".
     prune : bool, optional
         Indication of whether to prune short branches. The default is the
         global variable "PRUNE".
@@ -152,19 +148,6 @@ def build_neurograph_from_gcs_zips(
     t, unit = utils.time_writer(time() - t0)
     print(f"Module Runtime(): {round(t, 4)} {unit} \n")
 
-    # Generate proposals
-    if search_radius > 0:
-        print("Generate Edge Proposals...")
-        t0 = time()
-        neurograph.generate_proposals(
-            search_radius, n_proposals_per_leaf=n_proposals_per_leaf
-        )
-        t, unit = utils.time_writer(time() - t0)
-        print(
-            "# proposals:",
-            utils.reformat_number(len(neurograph.mutable_edges)),
-        )
-
     t, unit = utils.time_writer(time() - total_runtime)
     print(f"Total Runtime: {round(t, 4)} {unit}")
     print(f"Memory Consumption: {round(utils.get_memory_usage(), 4)} GBs")
diff --git a/src/deep_neurographs/neurograph.py b/src/deep_neurographs/neurograph.py
index 533d787..2bb5a44 100644
--- a/src/deep_neurographs/neurograph.py
+++ b/src/deep_neurographs/neurograph.py
@@ -116,9 +116,10 @@ def __add_nodes(self, nodes, key, node_ids, cur_id, swc_id):
             node_ids[i] = cur_id
             self.add_node(
                 node_ids[i],
-                xyz=nodes[key][i]["xyz"],
+                proposals=set(),
                 radius=nodes[key][i]["radius"],
                 swc_id=swc_id,
+                xyz=nodes[key][i]["xyz"],
             )
             if key == "leafs":
                 self.leafs.add(cur_id)
@@ -173,6 +174,8 @@ def generate_proposals(
                 # Add edge
                 edge = frozenset((leaf, node))
                 self.proposals[edge] = {"xyz": np.array([xyz_leaf, xyz])}
+                self.nodes[node]["proposals"].add(leaf)
+                self.nodes[leaf]["proposals"].add(node)
 
         # Check whether to optimization proposals
         if optimize:
@@ -264,9 +267,10 @@ def split_edge(self, edge, attrs, idx):
         new_node = len(self.nodes) + 1
         self.add_node(
             new_node,
-            xyz=tuple(attrs["xyz"][idx]),
+            proposals=set(),
             radius=attrs["radius"][idx],
             swc_id=attrs["swc_id"],
+            xyz=tuple(attrs["xyz"][idx]),
         )
         self.__add_edge((i, new_node), attrs, np.arange(0, idx + 1))
         self.__add_edge(