On the external set -the most challenging scenario -differences between models are more evident. DeepCocrystal consistently outperformed the benchmarks (Table ). Deep-Cocrystal achieved 15%-21% higher Balanced Accuracy, 21%-28% higher Precision, and 12%-56% higher Specificity than the benchmarks, albeit with a moderate Recall reduction (of up to 15% lower). Moreover, when analyzing different ranges of similarity (see Supporting Information, Table ), the advantages of DeepCocrystal become particularly evident for molecules with a similarity below 0.6, where Precision and Specificity are respectively 21%-35% and 27%-69% higher than the benchmarks. These results indicate that DeepCocrystal finds a better trade-off between correctly identifying positive and negative pairs than the benchmarks, even when applied to more diverse molecular pairs. The benchmarks predict positives more frequently (higher Recall), at the cost of more false positives (lower Precision and Specificity). This might be due to the skewed distribution of their training data and the unrealistic proportion of positive pairs. On the contrary, DeepCocrystal shows a superior performance in minimizing false positives, thanks to SMILES augmentation (as visible from the SMI-CNN controls), making it a more effective tool for accelerating co-crystallization screening.