Overall, there were signs that for a virtual screening or hit finding application, the 3DGNN model trained including drug discovery LO training datasets achieved the highest performance with an EF at top 1% of 9.0 followed by the 2DCNN model and GLIDE with 7.3 and 6.6, respectively. The best model trained without LO datasets (3DGNN) achieved an EF at top 1% of 6.3. The 3DGNN was not only performing the best on average in terms of enrichment but also was more frequently the best model for a given target dataset. The models were resilient for challenging systems where there are ions and loop flexibility (PDE2, PDE10 and KDR). Hit enrichment is important, but also the quality/diversity of the hits. Molecular weight bias is also present in LO datasets, where hits are grown to improve their affinity during lead optimization. We evaluated the hit enrichment factors at different MW ranges and the similarity among the top scored active compounds. DL models were able not only to retrieve more hits, but also this was accomplished in all MW ranges. This suggests that the models can provide better enrichment at more attractive lower molecular weight ranges. Additionally, we measured the average similarity for top predicted active compounds. All methods show a similar average similarity demonstrating there is no clear advantage for any method in retrieving more diverse hits.