The obtained experimental results were used to evaluate the performance of the benchmark models retrospectively (Table ). Although such evaluation is somewhat limited due to the experiments being selected with DeepCocrystal, it offers additional insights into model comparison on external data. SMILES augmentation resulted pivotal to achieve these results. SMI-CNN(can) predicted all purine derivate coformers as 'positive' for co-crystallization, yielding 60% false positives overall. These findings indicated that (a) SMILES augmentation allowed DeepCocrystal to better capture small structural changes that might be relevant for co-crystallization, and (b) that the uncertainty estimation allowed to further reduce the number of false positives (Supporting Table ). All literature benchmarks struggled in differentiating molecules with similar chemical structures (e.g., all purines were classified as positive), and led to many false positives (Precision consistently below 50%).