We benchmark the proposed methodology on a subset of the MoleculeNet benchmark for molecular machine learning and compare it against a selection of non-pretrained baselines that are commonly used for chemical property prediction. RF and SVM use binary extended-connectivity fingerprints, ECFP, as input, graph neural networks GCN and GIN use the molecular graph as input , SchNet and MGCN take graphs as input and explicitly model quantum chemical interactions within molecules , and D-MPNN is a directed message-passing neural network that takes graphs and molecular descriptors as input . The current SOTA on the benchmark has been achieved by Molformer-XL , which was pretrained on 1.1 billion molecules for approximately 208 hours on 16 NVIDIA V100 GPUs and then fine-tuned for another 12 hours, has not been included in Tables and as we focus on non-pretrained methods. Furthermore, we extended MolZip towards predicting proteinligand binding affinities and compared the approach with GNN-based methods, which have seen continuous use and advancements over the past years. For all our experiments, we have chosen Gzip as the compressor as it generally shows the best performance when compared to LZ4 and Snappy (Table .2). Finally, we ran additional experiments for knearest neighbour-based classification and regression using ECFP as a control.