diff --git a/graphs/bfs_shortest_path.py b/graphs/bfs_shortest_path.py
index 1655ca642..754ba4035 100644
--- a/graphs/bfs_shortest_path.py
+++ b/graphs/bfs_shortest_path.py
@@ -1,8 +1,6 @@
 """Breadth-first search shortest path implementations.
-
     doctest:
     python -m doctest -v bfs_shortest_path.py
-
     Manual test:
     python bfs_shortest_path.py
 """
@@ -19,22 +17,19 @@ graph = {
 
 def bfs_shortest_path(graph: dict, start, goal) -> str:
     """Find shortest path between `start` and `goal` nodes.
-
     Args:
         graph (dict): node/list of neighboring nodes key/value pairs.
         start: start node.
         goal: target node.
-
     Returns:
         Shortest path between `start` and `goal` nodes as a string of nodes.
         'Not found' string if no path found.
-
     Example:
         >>> bfs_shortest_path(graph, "G", "D")
         ['G', 'C', 'A', 'B', 'D']
     """
     # keep track of explored nodes
-    explored = []
+    explored = set()
     # keep track of all the paths to be checked
     queue = [[start]]
 
@@ -61,7 +56,7 @@ def bfs_shortest_path(graph: dict, start, goal) -> str:
                     return new_path
 
             # mark node as explored
-            explored.append(node)
+            explored.add(node)
 
     # in case there's no path between the 2 nodes
     return "So sorry, but a connecting path doesn't exist :("
@@ -69,16 +64,13 @@ def bfs_shortest_path(graph: dict, start, goal) -> str:
 
 def bfs_shortest_path_distance(graph: dict, start, target) -> int:
     """Find shortest path distance between `start` and `target` nodes.
-
     Args:
         graph: node/list of neighboring nodes key/value pairs.
         start: node to start search from.
         target: node to search for.
-
     Returns:
         Number of edges in shortest path between `start` and `target` nodes.
         -1 if no path exists.
-
     Example:
         >>> bfs_shortest_path_distance(graph, "G", "D")
         4
@@ -92,7 +84,7 @@ def bfs_shortest_path_distance(graph: dict, start, target) -> int:
     if start == target:
         return 0
     queue = [start]
-    visited = [start]
+    visited = set(start)
     # Keep tab on distances from `start` node.
     dist = {start: 0, target: -1}
     while queue:
@@ -103,7 +95,7 @@ def bfs_shortest_path_distance(graph: dict, start, target) -> int:
             )
         for adjacent in graph[node]:
             if adjacent not in visited:
-                visited.append(adjacent)
+                visited.add(adjacent)
                 queue.append(adjacent)
                 dist[adjacent] = dist[node] + 1
     return dist[target]