To tackle the challenge of protein-ligand binding affinity prediction using MolZip and MolZip-Vec, we implemented a data loader capable of loading and concatenating different modalities, namely SMILES and amino acid sequences, and pass it to a MolZip or MolZip-Vec regressor. As we evaluated the method on the PCBbind data set , the following information was provided for each protein-ligand complex: (i) structural and compositional data for the ligand, (ii) structural and compositional data for amino acids that are part of the binding pocket of the protein, and (iii) structural and compositional data for the entire protein. From this data, we generated the following encodings: (1) For the ligand, a SMILES string, (2) for the binding pocket, a SMILES string and a one-letter amino acid string sequence, where amino acids that are not part of the binding pocket are replaced by an X, and (3) for the protein, a one-letter amino acid string sequence. These encodings provided us with four modalities (one molecule representation, two binding pocket representations, and one whole-protein representation) that can be combined arbitrarily through concatenation. Exploratory benchmark results for the combinations ligand (SMILES), binding pocket (SMILES), binding pocket (amino acid sequence), wholeprotein (amino acid sequence), ligand (SMILES) + binding pocket (SMILES), ligand (SMILES) + binding pocket (amino acid sequence), and ligand (SMLIES) + wholeprotein (amino acid sequence) can be found in Table .4. The combination ligand (SMILES) + binding pocket (amino acid sequence) provided the best results. Comparing our best results against baseline GraphDTAand GNN-based methods, it becomes evident that MolZip performs exceptionally well. It does not only perform better than basic GNNs, including GCN, GAT, and GIN, that used atom features as node attributes for the molecular graph and the protein sequence as inputs , but also better than methods that include geometric information in the form of atom-wise protein-ligand interactions, such as GNN-DTI . The introduction of molecular descriptors with MolZip-Vec reduces the performance to that of GraphDTA methods, hinting at the importance of a relatively fuzzy representation of the ligand to a wellperforming compression-based model.