The shortest path between two locations is crucial for location modeling, spatial analysis, and wayfinding in complex environments. When no transportation system or network exists, continuous space movement adds substantial complexity to identifying a best path as there are increased travel options as well as barriers inhibiting potential movement. To derive the shortest path, various methods have been developed. Recent work has attempted to exploit spatial knowledge and geographic information system functionality, representing significant advantages over existing methods. However, a high density of obstacles increases computational complexity making real‐time solution difficult in some situations. This article presents a spatial filtering method to enhance Euclidean shortest path derivation in complex environments. The new approach offers substantial computational improvement while still guaranteeing an optimal path is found. Application results demonstrate the effectiveness of the approach and its comparative superiority.