The separation space is dominated by reversed-phase chromatography (RPLC) and gas chromatography. For RPLC in particular, there is often a linearity assumption between the hydrophobicity and the size of a chemical and its retention within the set experimental setup. There are several reversed-phase liquid chromatography studies where the retention time of the internal standards are correlated to their octanol/water partitioning coefficient. These linear models are then extrapolated to infer which portion of the chemical space is covered. This linearity assumption has been challenged by different studies focusing on retention time modeling. A recent study has shown that chemicals with similar retention behavior in RPLC may have up to six orders of magnitude variance in their predicted partition coefficients. In the same study, a data driven approach showed that 20,000 chemicals present in Norman SusDat (around 100 k unique structures) are not analyzable with RPLC. In the detection space the ionization efficiency (IE) is the main determining factor for the measurability of a chemical, which fundamentally and potentially significantly limits the measurable space covered. The IE is a structure dependent parameter indicating the magnitude of the generated signal for a specific chemical. There have been several recent studies that have successfully predicted the IE of known and unknown structures. Therefore, the IE could be used as a parameter for categorizing chemicals as detectable or not detectable based on their IE. It should be noted that there has been a study classifying the chemicals to analyzable via GC-MS vs LC-MS. However, that study did not emphasize the measurability bottleneck and has been trained based on well-known structures (e.g. simple hydrocarbons and pharmaceuticals).