By applying the proposed Gzip-based text classification method by to multiple molecular classification tasks and extending it to regression problems, we verified its validity and utility beyond natural language process-ing tasks. While the claims by in regards to performance comparisons with large language models were rather optimistic, the adapted methodology showed good performance compared to often-used fingerprint-and graph-based methods in a chemistry and biochemistry setting. Furthermore, it is in itself highly intriguing that a method based on differences in the length of Gzip compressed string representations of molecules can yield comparable or even superior performance compared to methods that were and continue to be in use for more than a decade. We have also shown that the methodology can be extended to multimodal binding affinity tasks, where SMILES strings and amino acid sequences are jointly compressed. On the PDBbind data set, our proposed method performs better than all GraphDTA-and most GNN-based methods, including those incorporating spatial information. We belief that this method represents a superior baseline for future developments compared to other approaches, as it is exceptionally easy to reprudce-a problem often encountered in highly parameterised methods. Additionally, we have demonstrated that integrating molecular SMILES strings with string-converted chemical descriptors can significantly enhance the accuracy compared to using SMILES input alone. Finally, we discuss how such a method could be of interest outside machine learning and support a new generation of chemical information retrieval in ultra-large databases. However, certain limitations and challenges still need to be addressed, including the relatively high time complexity of the kNN-based approach and the elucidation of the reasons for significant gaps in performance on specific data sets compared to the state-of-the-art.