With this initial foray into molecular set representation methodology, we were able to show competitive results of the technique across a wide range of use cases, including the prediction of quantum-chemical properties, pharmacokinetic properties, binding affinities, and reaction yields with minimal hyperparameter adjustments and consistently straightforward architectures. Our most straightforward model, MSR1, which is essentially a set of ECFP fingerprints with a radius of zero, performs as well or better than D-MPNN on 5 out of 11 and better than GIN(E) on 9 out of 11 of the tested MoleculeNet benchmark data sets. With the poorer performance of the purely set representation-based methods MSR1 and MSR2 compared to SR-GINE, GINE, and (D-)MPNN on more recent data sets, we have shown that the commonly used benchmarks provided by MoleculeNet and other cheminformatics and machine learning libraries may not be well-suited to benchmark GNN architectures, as including explicit molecular topology does not seem to provide a significant advantage. Given the past and current reliance on these data sets of researchers when benchmarking new molecular deep learning architectures, we conclude and suggest that future architectures should be evaluated on other data sets, including those made available by Bhat et al. [24] and Fang et al. [31].