Bayesian (Tree Parzen Estimator as implemented in the hyperopt package ) search iterations. The fine-tuned hyperparameters include the depth and hidden size of message passing layers, the number of layers and hidden size in the FFNNs, and the dropout rate. We evaluate the models' ability to predict QM descriptors using random and Bemis-Murcko scaffold splits. The scaffold split is considered to be a more rigorous approach to measuring a model's generalizability, as it is based on molecular backbones. Each model is trained for 50 epochs during hyperparameter optimization and final model training.