Recently, a parameter-free text classification approach based on Gzip compression has been proposed, which has shown excellent performance compared to deep learning architectures, such as transformers, on text-classification benchmark data sets . The intuition guiding the method is to exploit the capability of lossless compressors, such as Gzip, to capture regularity using a statistical model that enables to assign shorter codes to high-probability sequences. It is then assumed that texts in the same category share similar regularity and are thus close in compression space under a normalised compression distance (NCD) metric . A k-nearest neighbour classifier is then used to classify text under the NCD metric. As the SMILES string encoding of molecular graphs was proved to be a well-performing molecular representation for applying NLP methods, such as transformers or locality-sensitive hashing (LSH) , we hypothesise that the methodology presented by will also yield good results for chemical tasks, and a comparison to other commonly used methods is warranted.