Overall, we developed four models for exploration of the RPLC subspace. The r i regression models showed that fingerprints can be used for describing RPLC retention indices. The RPLC classification model was built with a focus on small organic molecules (i.e., ≤1000 Da). The model did overall have more difficulties with regard to more bulky and branched or surfactant-like chemicals as well as metal-organic compounds. Additionally, the model was not able to properly predict the RPLC subspace class of chemicals that are organic complexes, due to the fact that in solution those are dissociated into multiple individual structures. The latter is not a major limitation for the model itself, since, using expert knowledge, they can be easily identified. Generally, as knowledge on analyzable chemicals with RPLC grows, the model could easily be rebuilt and expanded for the range of analytes.