The evaluation includes three machine learning algorithms for DR modelling: random forests (RF), support vector machines (SVM), and deep learning (graph neural networks or GNNs). Separately, we use GNNs for the SD modelling step. The same hyperparameter optimisation strategy and the same steps for determining the most suitable deep learning architecture as in the aforementioned study were are also applied. When designing models for MF-PCBA, we recommend evaluating in terms of 'DR only' models, which are trained exclusively on confirmatory activity data, and 'SD + DR' models that integrate the two modalities. In terms of performance metrics, we recommend the use of the R 2 , coupled with an error metric such as the MAE (mean absolute error) or RMSE (root mean square error) for regression tasks, and Matthew's correlation coefficient (MCC) coupled with the AUROC (the area under the receiver operating characteristic curve) for classification. The choice is based both on recent recommendations from the literature [CWJ21; CTJ21] and the nature of the data.