Figure and table show the performance of the different model architectures trained with and without internal data. Overall, the performance is poor, with low correlation coefficients in the range of 0.0 to 0.29 but in most cases a small improvement in performance is seen for those models trained with internal data. This improvement is observed for the 3DGNN and 2DCNN neural network architectures. On the other hand, PIGNet and the 3DCNN models are not benefiting from using internal data, rather the opposite. Importantly, none of the models built from the different architectures outperform GLIDE molecular docking software that shows the best average correlation. Based on our experience with models of this type we anticipated poor results to predict the absolute binding affinity for a set of entirely new protein targets. Afterall, the models treat the binding process in a highly simplified manner. However, despite the poor correlation metrics, there is an improvement in performance when using the additional LO data for training. Figure We therefore sought to understand if the models could still be used in a virtual screening scenario, asking the question: are they enriching the selection of more active compounds amongst the better ranked molecules? For this we calculated the enrichment factor (EF) to assess whether the models still show discriminating power against inactive compounds. Given that this is one of the main aims of this study, and we are principally interested to learn about 3D ML affinity prediction as a competitive computational tool, we again included the GLIDE docking software performance.