Machine learning -which automatically extracts relevant information from chemical datasets -can aid in prioritizing API-coformer pairs for co-crystallization . Several approaches have been used for co-crystallization prediction, ranging from 'human-engineered' molecular descriptors with tree-based models and neural networks to deep representation learning, using graph convolutional networks . In co-crystallization prediction, machine learning might struggle to generalize to previously unseen molecules . This is in part due to limitations of training datasets, which are unrealistically imbalanced towards 'positive' API-coformer pairs . In fact, unlike positive results, negative co-crystallization results may stem from either the inability of two molecules to co-crystallize or suboptimal experimental conditions, leading to a general under-reporting of negative data . To counterbalance this issue, random labeling of untested molecule pairs as negatives has been proposed as a solution , at the risk of introducing mislabeled data. At the same time, undersampling approaches could rebalance positive and negative molecule pairs, but they might distort data distribution, and lead to overfitting compared to the real-world occurrence . Hence, developing machine learning approaches that are less sensitive to the limitations of current co-crystallization datasets and perform well on previously unseen molecular pairs bears great potential to advance API-coformer prioritization.