Together with the results on QM7 and QM8 (Table ), the results of the OCELOT chromophore benchmark suggest that set representation learning can perform as well as graph representation learning on physico-and quantum-chemical prediction tasks. Furthermore, combining graph and set representation learning, as is done with SR-GINE, exhibits synergistic effects that result in overall enhanced performance. Specifically, the significant performance increase of SR-GINE over GINE indicates that a set representation layer in lieu of a global pooling function can improve existing GNN architectures.