Here, we introduce several of what we denote molecular set representation learning architectures based on the scheme described above and in Figure , and evaluate them against widely used GNN methods on a wide array of tasks, including physico-and quantum-chemical property prediction for material science, pharmacokinetic property prediction for drug design, protein-ligand affinity prediction for drug discovery and biochemistry, and reaction yield prediction for synthetic and process chemistry. We show that using the concept of molecular set representation learning and combining it with GNN architectures, we can not only simplify current approaches but also improve on commonly used GNN implementations such as D-MPNN, GAT, or DimeNet . Furthermore, we uncover that compared to more modern benchmark data sets, extensively used older benchmark data sets may not be well-suited to evaluate the advantages of GNNs, as they perform worse than the most simple of our models. Finally, we introduce an easy-to-use, extensible collection of molecular set representation architectures ready to be used in various fields, including materials science, drug discovery and development, biochemistry, and synthetic and process chemistry.