Currently, there are substantial efforts on compiling larger databases of published experimental datasets from the literature. However, as we demonstrated, only a small subset, ∼ 5000 points, of our training set are needed to achieve near-optimal performance. Compiling larger databases will not continue to improve model performance beyond the aleatoric limits demonstrated here. Instead, accurate datasets of organic solubility are needed, analogous to the CheqSol dataset compiled by Palmer and Mitchell. While it is possible that further innovations in model architecture can improve predictions, the irreducible error imposed by the experimental uncertainty must be reduced in order to impart a noticeable improvement in organic solid solubility predictions.