Compression-based representation of molecules may have implications beyond machine learning. As chemical databases such as ZINC or GDB contain billions of molecules, and even partially human-curated databases such as PubChem contain more than 100 million unique molecules, retrieving information based on chemical features is becoming increasingly important . Currently, most searches rely on graph topological similarity based on molecular fingerprints, precomputed stored chemical descriptors, or a combination of both . With the findings presented in this study, we have shown that the lossless compression-based combination of molecular structure and chemical descriptors, used as an input for MolZip-Vec, presents a low-memory alternative to established methods discussed by that allows for direct structure and property-based storage, similarity search and indexing.