Training of benchmark models was carried out using hyperparameters optimized for each task. Throughout the remainder of this study, we classify datasets as small/large if they contain less/more than 10k data points in total. Models trained on small datasets were optimized for hyperparameters using 100 search iterations, whereas models trained on large datasets were optimized for only 30 iterations. During hyperparameter tuning and final model training, we trained for 200/50 epochs for small/large datasets. All models were trained on a single data split with an ensemble size of 5 during the final training, and without ensembling for hyperparameter tuning. During hyperparameter tuning, we optimized for the number of message passing steps, the hidden size during message passing, the number of layers of the feed forward neural network, as well as its hidden size, and the dropout ratio.