With the introduction of graph neural networks (GNN), graph representation learning for molecules attracted a growing interest, as these neural network architectures allowed representation learning directly on the molecular graphs rather than first extracting features or precalculating descriptors from a given chemical structure , with newly proposed methods continuously escalating the architectures' complexity or amount of precomputed or, less likely, experimentally measured molecular properties, such as 3D coordinates or structural motifs, that are added to the graph . However, even though the published literature often touts the benefits of introducing new features based on molecular composition and topology, it is generally ignored that these features are also implicitly encoded by lower-order representations.