Furthermore, we showed that introducing a set representation layer in place of a global pooling function in a GNN (specifically a GINE) improves its performance in virtually all benchmarks and, in more recently released data sets, performs equal or better than MPNN and D-MPNN without the need to introduce additional precomputed molecular descriptors. This insight may be used to extend and improve the performance of all currently used GNN-based molecular representation approaches. In addition, we introduced a set-based model for protein-ligand binding affinity prediction, which allows for the introduction of implicit geometric information through a radius near neighbour search between the protein and the bound ligand, allowing the model to perform better than existing graph-based approaches, which often cannot integrate such information easily. Finally, our conceptually naïve set-based reaction yield prediction model more than doubles the performance (R 2 ) of YieldGNN on the electronic lab notebook-extracted (ELN) data set of Buchwald-Hartwig cross-coupling reactions and matches the performance of established methods on the high-throughput experiment (HTE) data.