In addition to molecular property prediction, we tested the ability of the compression-based approach to predict protein-ligand binding affinities-an essential metric for rational drug design, which aims to find a drug candidate, given structural information on a disease-associated protein . The protein-ligand binding affinity describes whether and how strong a ligand binds non-covalently to a protein, usually causing a conformational change of the protein and potentially leading to a therapeutic effect . The prediction of the binding affinity, given a potential ligand and a protein's structure or amino acid sequence, is therefore of interest to computational chemistry. Over the past years, geometric deep learning, specifically graph neural network-based approaches, have emerged as the most investigated methods to predict binding affinities, as they are capable of capturing topological and spatial features important to protein-ligand binding .