Path planning on gridmaps is a common problem in AI and a popular topic in application areas such as computer games. Compressed Path Databases (CPDs) represent a state-of-theart approach to the problem, in terms of the speed of computing full optimal paths and also individual optimal moves. Despite significant improvements in recent years, the memory required to store a CPD can still be a bottleneck for large game maps. In this work we present a new compression approach that can reduce the size of CPDs. Our approach uses an extended notion of wildcards and a novel concept called a redundant symbol. We implement our ideas on top of a state-of-the-art CPD system and, in a range of experiments, we demonstrate a substantial reduction in the size of CPDs.