The dataset for building the RPLC classifier model was comprised of three classes: 'inside', 'maybe', and 'outside' the RPLC subspace (Figure ). The 'outside' chemicals were obtained from the NORMAN SusDat database based on their extreme XLogP values, assuming that these cannot be analysed using RPLC regardless of the method used. Here, the XLogP was chosen rather than the logD due to the fact that it is easier to predict, more stable, and more accurate. For the 'outside' case, a total of 3999 compounds with a XLogP value above 10 or below -10 and with a molecular weight below 1000 Da were obtained. As for the 'inside' and 'maybe' chemicals, these were obtained from the experimentally defined r i values by the three r i series. For each of the series, the absolute difference between the predicted and measured r i (i.e., the residuals) versus the measured r i values were plotted and the regions of extrapolation were identified. These regions were obtained based on the increasing residuals that were caused by the inherent over estimation and under estimation of a RF regression model, which are associated with either extremely low or extremely high r i values, respectively. These regions correspond to chemicals that elute close to t 0 or are very difficult to elute from the column (i.e., require a relatively high percentage of organic modifier). The chemicals with a measured r i in these extrapolation regions were labeled as 'maybe' and the remaining chemicals were labeled as 'inside' the RPLC subspace. This yielded a total of 620 'maybe' and 5167 'inside' compounds. Whenever a chemical SMILES was found in multiple classes (i.e., it was present in multiple datasets of the r i models), it was removed from the lower ranking RPLC classes and kept in the highest ranking RPLC class (i.e., 'inside' > 'maybe' > 'outside' RPLC class rank). For example, if a chemical was found in the 'maybe' region for UoA and in the 'inside' for Cocamide, it would be classified as 'inside'. More details on the division between the 'inside' and 'maybe' classification can be found in Section 'RPLC Classification Model' as these are based on the results of the three RF regression models. It should be noted, that even though output information from the r i models has been used to set up the classification dataset, the regression and classification models are independent, meaning that there is no data leakage taking place.