Uncertainty quantification is crucial whenever the model predictions are used to steer experimental design, since unforeseen prediction errors can lead to wasted time and resources. To the best of our knowledge, no approach to date incorporates uncertainty estimation when predicting the cocrystallization of molecular pairs. To extend the applicability of DeepCocrystal to real-world scenarios, we equipped it with an estimate of its (un)certainty. Here we use the term 'uncertainty' in its broad sense, i.e., as a measure of confidence in the model's predictions . We represented each molecular pair with ten different (pairs of) SMILES strings, and used DeepCocrystal predictions to estimate uncertainty. Specifically, whenever a given molecular pair received conflicting predictions across its SMILES repetitions, we considered the model predictions as potentially unreliable. This was substantiated by the observation that the average model prediction across SMILES repetitions highlighted several errors (Fig. ). We tested two ways of estimating the DeepCocrystal's uncertainty using its predictions on the SMILES repetitions for each molecular pair: (a) Majority voting, whereby the number of agreements in the predicted class per each molecular pair is used as a measure of confidence (the higher, the better); and (b) Standard deviation-based estimation, whereby the standard deviation across augmented SMILES (per each pair) is computed (the lower, the better). For each approach, several thresholds of uncertainty (i.e., on standard deviation or on number of agreeing predictions) were evaluated for their effect on performance and on the number of molecules retained for prediction (Table ).