In terms of measurable exposome chemical space, the combination of the modeled separation (e.g., retention time) and mass spectral behavior of chemicals can be used. Retention indices can provide the first glance into the connection between the structure of a molecule and its behavior in the chromatographic space. On the other hand the ionization efficiency has great potential in connecting the structure of a chemical to its response in the mass spectrometer. The combination of these two metrics can provide a valuable training set to build models where measurability of a chemical can be assessed based on its structure. A potential by product of such a strategy is that these models may be able to suggest the optimized experimental conditions for the analysis of a certain structure (e.g. reverse phase vs normal phase). In addition, the development and integration of models using complementary separation techniques in parallel to chromatography are needed (e.g., electrical separations such as capillary electrophoresis (CE) and ion mobility spectrometry). Ultimately, modeling multiple separation spaces using data from orthogonal techniques may reduce porosity and extend the boundaries of the measured chemical space, as well as provide additional confirmation where overlaps exist (e.g., machine learning-based prediction of both retention behavior and collision cross section may increase confidence in prioritization workflows ).