The overall workflow for this work can be found in figure and the details are explained in the following sections. In brief, a total of four random forest (RF) models were built, of which three were r i RF regression models (Figure ) and the fourth a RPLC RF classification model (Figure ). For building these models, a type of molecular fingerprint needed to be selected and the dataset obtained before model optimization and performance testing (Figure ). These models were used for evaluating the potential of using molecular fingerprints for prediction of retention behavior in RPLC and for setting up two of the classes for the fourth RF classification model. The latter refers to the 'inside' and 'maybe' inside class. Here, the 'maybe' class represents the chemicals that are poorly retained (i.e., close to t 0 ) or require relatively high amounts of organic modifier to elute, meaning that these compounds can generally be difficult to analyze and require specific methods. All chemicals in between the 'maybe' regions are classified as 'inside'. For the RPLC classification model, a dataset with chemicals that were 'inside', 'maybe' inside, and 'outside' of the RPLC subspace was constructed (Figure ). Finally, the application of the RPLC classification model was showcased by applying it on the NORMAN SusDat database, which is a collection of expert curated environmentally relevant chemicals that have been actively used for screening of complex samples. All training and test datasets for constructing the models and the NORMAN SusDat database with the calculated fingerprints can be found on Figshare. 31