Here, we report an implementation of the Gzip-based text representation method, initially introduced by , targeted towards chemical machine learning prob-lems. We present two algorithms denoted MolZip and MolZip-Vec, both capable of single-and multimodal molecular classification and regression, with MolZip-Vec also allowing for the incorporation of real-valued vectors to embed precomputed chemical values. We compare our implementation to various other methods that do not rely on pretraining, including molecular fingerprint-based approaches and graph neural networks (GNNs), on molecular classification and regression tasks that include a binding-affinity prediction problem which we cast as a multimodal task by including the molecular SMILES of the ligand and the amino acid sequence of the protein. We show that this conceptually simple and inexpensive method works not only for the classification and clustering of data in a natural language processing context but also on SMILES-encoded molecules without requiring time-consuming training on specialised hardware, such as GPUs. In addition, we extend the methodology to support most chemical machine learning tasks through an open-source Python library.