Again, the inclusion of the LO data resulted in improved performance for all models except for the 3DCNN (Table ). Notably, the 3DGNN model trained with internal LO data showed the best performance in both Pearson correlation coefficient and enrichment in the top 1% with averages of 0.5 and 10.70 respectively. GLIDE appears as the second best in terms of correlation coefficient (r 0.47), while the 3DGNN trained without the LO data ranked second in terms of enrichment factor (9.53). The 3DGNN model trained without internal data ranked second in average enrichment factor (9.53), but with a noticeable drop in Pearson correlation coefficient (0.30). Interestingly, the PIGNet model trained with internal data achieved 3 rd position in terms of enrichment factor, with very low correlation coefficient. However, average enrichment factor for this model is biased by the disproportionate performance in a single dataset (MAPK, Figure ). The 2DCNN architecture ranked 5 th in average enrichment factor (7.19), showing on the other hand low average correlation coefficient (0.17 docking, especially the 3DGNN. In the range between 250-300 Daltons GLIDE showed better performance.