Hit diversity analysis. We investigated whether the models were predicting the bioactivity of chemically diverse or similar molecules in comparison to GLIDE as a representative state of the art docking software. We computed the average similarity among the top 100 active molecules (real active compounds) ranked by each method (Table ). The results show that the 3DGNN returned the most similar actives with an average Tanimoto similarity of 0.6, while PIGNet produced the most diverse set with an average similarity of 0.49. However, these differences were dependent on the dataset, as the differences were smaller between models within most of the datasets (Figure ). External ChEMBL LO test sets. To add further diversity to the test sets, and consider data available in the public domain, we assessed the performance of our models on prepared ChEMBL datasets, as described in the Methods, both with and without the drug discovery LO training data.