In this paper, we present a data driven approach for a generic framework that enables quick screening of the RPLC chemical space, assuming that the molecules are in solution and can be injected into a system. A set of regression and classification models were built to assess whether a structure can theoretically be analyzed via RPLC. To build the RPLC classification model, firstly, we show the potential of using fingerprints for the prediction of r i values for three retention index series, confirming that molecular fingerprints contain information on RPLC retention behavior. Three commonly used scales, namely: the n-alkylamide system, containing the n-alkylamide homologous series from n-propanamide to n-tetradecanamide (C3-C14) , the r i system developed by Aalizadeh et al. from the University of Athens referred to as UoA, comprising of 18 reference compounds that were computationally selected in order to achieve a broad and reliable r i reference system , and the cocamide diethanolamine homologous series that is comprised of C(n = 0-23)-DEA chemicals were employed for our model building. Secondly, we show the performance of the RPLC classification model and apply the model on a set of 91737 small molecules (i.e., molecular weight ≤ 1000 Da) from the NORMAN substance database (SusDat).