Recently, Brocidiacono et al. used SNA to randomly select decoys instead of curating them to each target based on molecular properties, while ensuring that the decoys did not belong to the existing distribution of ligands for each target using pocket similarity and ligand clustering measures. Here, SNA is used to simply mean the random sampling of compounds from the ChEMBL database after removing any compounds that were present in our dataset. This was done based on the assumption that the chance of any random compound binding , SNA significantly influenced AUROC and PFI metrics, while having negligible effects on the F1-score. Further analysis segmented by data splitting strategy reveals a substantial impact of SNA on all three metrics (Table ). In posthoc pairwise comparisons, we observe an increase in AUROC by 0.09 in the baseline condition (Supp. Table ) but almost no difference in F1-score (Supp. Table ) or PFI (Supp. Table ). This is consistent with Brocidiacono et al's findings that SNA improved AUROC without a significant effect on F1-score. The stable F1-score, despite more balanced class distributions, indicate that overall model performance was unaffected by improved class balance, suggesting limitations in using AUROC as the sole metric for gauging performance improvements. Furthermore, the stability of PFI following SNA implementation suggests that this strategy did not increase the predictive contribution of protein or ligand features to the model. However, the results per data split in the baseline condition revealed some unexpected findings. Despite the implementation of SNA, the F1-scores remained largely unchanged across all splits (Supp. Table and Figure ). Consistent with the earlier discussion, the AUROC demonstrated more notable variations; it increased by 0.19 in the kinase split and by 0.07 in the family split (Supp. Table ).