In practice, ML models are trained on a first set (referred to as training set) and tested on a second distinct set (testing set), while hyperparameters may be optimized using a third set (validation set). While simple random splitting is still often used, the similarity between these sets must be monitored and minimized. If the model memorizes input data ("CH3COOH: pKa 4.75") rather than learning to predict a property (e.g., "electronic effects make acetic acid acidic, with a pKa of 4.75"), the model tends to be poorly predictive (e.g., "CF3COOH looks like CH3COOH and is predicted to have a pKa of 4.75", as opposed to "the fluorine inductive effect reduces the pKa to 0.2"). If similar compounds are kept in the testing set, memorization would still yield a good prediction ("C2H5COOH looks like CH3COOH and is predicted to have a pKa 4.75"). This would lead to an overestimation of the real accuracy of the model. Available datasets. When MM or QM methods are used, developers often rely on sets of a few dozens of systems to test their methods. However, as more data-intensive ML-based methods become more prevalent, and the need for datasets arises, a few curated datasets have been reported and made available.