This study shows the potential of DeepCocrystal to advance the state-of-the-art. DeepCocrystal owes its performance to the intriguing properties of the SMILES language, which allowed mitigating data imbalance and estimating uncertainty. By learning (and then combining) single-molecule information, DeepCocrystal learns elements of the "supramolecular language" of co-crystal formation. Compared to existing approaches, DeepCocrystal is particularly beneficial to minimize the number of false positives, which is crucial for prospective application campaigns. The experimental validation of DeepCocrystal further corroborated its potential and identified adenine and caffeine as two previously unreported coformers of diflunisal. These results, taken together, underscore the potential of DeepCocrystal to accelerate the discovery of co-crystallization partners.