The increased data density facilitates the learning of SAR and reduces the biases associated to sparse data, like MW bias and the classification of activities by targets. Specifically, the Graph Neural Network (3DGNN) consistently showed better enrichment and MW diversity across most of the datasets in our external tests using LO project data compared to molecular docking scoring function. However, our models are not yet capable of providing a strong correlation between experimental and predicted activities. A particular limitation is the static representation of the system which underestimate the impact of the dynamics and the conformational changes, entropy and (de)solvation. The quantity and quality of data required to properly rank compounds within the small activity range are unknown, and it is unlikely that they will be available in the short or medium term. Instead, we believe that incorporating more physics into DL models and providing a complete picture of the thermodynamic cycle, either from simulations or other DL models, is a more promising direction to pursue.