For the graph-based models (the GINE baseline and SR-GINE), we ran an initial simple search using the BBBP data set over both models while keeping the set representation parameters for SR-GINE fixed at 8 hidden sets with 8 elements each. For each set of hyperparameters, 6 models were trained on random seeds and their performance averaged. The best models were selected for lowest cross entropy loss during validation. During this search, SR-GINE performed consistently better than the baseline (GINE). In a next step, we conducted a further simple grid search on the number of hidden sets and elements, as the authors of the original publication have shown the performance to be influenced by these parameters . We chose 128 hidden sets with 64 elements each based on tests on the BBBP data set.