To show that fingerprints can be used to describe retention behavior in RPLC and for setting up the dataset for the RPLC classification model, random forest (RF) regression models were built using three different retention index series (Figure ). The three series used for this, were the amide , University of Athens (UoA) , and cocamide series. For each of the series, the measured r i were obtained from their respective articles, yielding 1485, 1818, and 3008 unique chemicals with measured r i values for the amide, UoA, and cocamide series, respectively. For all chemicals, the 2DAPC and PubChem fingerprints were calculated according to Section 'Fingerprint Calculations'. For each r i series, data was split into a training and test set, at random, with a ratio of 0.85:0.15, ensuring similar coverage of the r i range in both sets. The test set was only used for testing and thus never used for training. For optimization of the RF regression models, the training set was used with a 0.8:0.2 split for training and cross-validation, respectively. This ratio of split has been shown to be effective in such data sets. The RF regression models used a third of the features (i.e., 264)