The differential heuristic (DH) is an effective memory-based heuristic for explicit state spaces. In this paper we aim to improve its performance and memory usage. We introduce a compression method for DHs which stores only a portion of the original uncompressed DH, while preserving enough information to enable efficient search. Compressed DHs (CDH) can be tuned to fit any size of memory, even smaller than the size of the state space. Experimental results across different domains show that, for a given amount of memory, a CDH significantly outperforms an uncompressed DH.