Eight target datasets were used for training and the remaining were used as new, challenging, external test sets, Table (training) and Table (external test sets). An unusually larger proportion of datasets were used for external testing in this study as we are particularly interested to inspect the model performance in real-world scenarios, and the training data is still substantially augmented versus PDBbind alone. The number of experimental structures used for each of the protein targets ranged from 5 to 443, with a total of 811 and 1170 for the training and test sets. The total number of complexes in the training and testing datasets was 16318 and 27381 respectively. Thus, on average the inclusion of the docked close analogues led to a factor of 20-23 increase versus using only experimentally solved structures. The minimum and maximum number of protein-ligand complexes per target across the datasets was 384 and 3995. The overall range of affinity for all the training set data was from 3.1 to 11.8 and the mean range of affinity per target was 5.4 log units. We note how the total set of LO drug discovery data had a narrower overall range compared to PDBbind (8.7 vs 10 log units in total), but each given target had a wider range of 5.4 log units compared to ~2 log units in PDBbind.