For the GNN layer, we implemented and evaluated Graph Isomorphism Network (GIN), Graph Attention Network (GAT), and Graph Convolutional Network (GCN) as graph embedding layers for our set representation extended graph neural network . Again, we used default hyperparameters based on the original publications and, pairing each set representation layer with each GNN layer, evaluated different versions of our set representation-enhanced graph neural network. Using the same selection of benchmark data sets as with the set-representation layer selection, we evaluated the nine combinations of GNN and set representation layers (Tables and). Overall, the results point towards a need for hyperparameter optimization depending on the combination of models. We therefore picked the combination not on the performance shown in Tables and but the fact that we use RepSet in MSR1 and 2, and that GIN(E) is generally reported as a well performing baseline architecture in chemistry tasks .