Screening libraries are curated sets of compounds that can be used in various stages of the drug discovery process, typically containing hundreds to hundreds of thousands of compounds. Various classes of screening libraries have been collated, including diversity, hypothesis-led (or targeted), bioactive and fragment. Fragment libraries are typically used in hit identification, where medium-throughput screening can identify fragment hits against a target protein. When designing (or selecting) a fragment library, it is greatly beneficial to have the ability to follow up on a potential hit using commercially available structural analogues in the hit expansion phase. In this work, we consider the progressibility of a given fragment by using EVPs to analyze the diversity of substitution patterns in commercially available structural analogues. We note that progressibility is not directly related to synthesizability and a lack of a particular EVP in a large commercial vendor library does not necessarily mean the corresponding synthesis is intractable. Progressibility is a particularly relevant consideration when designing fragment libraries, since the route from hit to lead molecule is likely more convoluted than from hit matter discovered using traditional HTS libraries. If structurally related chemical matter cannot be readily purchased from vendors, then hit expansion must be facilitated by bespoke synthesis. This slows down the discovery process and entails additional costs. In this section, we show how a full evaluation of the different exploited vectors of molecular scaffolds can be used to assess the progressibility of each screening library fragment. Table shows the scaffold-level ensemble statistics for the example scaffolds in Figure , that could then be used to illustrate scaffold progressibility information useful for the selection of individual molecules in a fragment screening library. While the examples presented here are by design very simplistic, one can imagine a straightforward extension using similar approaches on datasets containing 10k-100k compounds. The example fragments in ChEMBL corresponding to Scaffold 2 from Figure (Scaffold 2 from Table ) are depicted in Figure with their associated EVPs. Scaffold 2 exhibits a nEVPS of 10, as shown in Table . The corresponding fragments have been grouped according to the number of EVs within the same scaffold, hereinafter referred as EV Degree or d EVP (1, 2, 3, 4). Of note, when calculating the EVP, distinct stereoisomers are considered as separate patterns. As illustrated in Figure , the last two enantiomers with the same [1,3,6,7] pattern are treated as unique EVPs. As shown in Table (correspondingly in Figure ), Scaffold 2 stands out with the highest nEVPS value of 10 from seven exit vectors. ) and their associated EVPs Figures and show the correlation between nEVPS and the nEXIT or %EXPLOITED respectively for the set of exemplar scaffolds in Figure and Table . Scaffolds 3, 5, 7 and 8 all share an nEVPS value of 7 each, corresponding to 6, 6, 8 and 7 exit vectors, respectively. In Scaffolds 1, 4, and 10, a relatively low nEXIT pairs with a shortage of exploited vector combinations, lead to a maximum nEVPS value of 3 amongst these scaffolds (Figure ). When considering a selection strategy for a screening library, In Figure , the correlation between nEVPS and the percentage of the exploited vectors is illustrated. It is interesting to examine the relationship between the two plots in Figures and. A notable example can be observed when analyzing Scaffolds 3 and 5 (Table and Figure ). Despite sharing identical values of nEVPS (7) and nEXIT (6), they display different levels of %Exploited, with Scaffold 3 at 100% and Scaffold 5 at 67%. While these two scaffolds coincide in the nEVPS versus the nEXIT graph in Figure , the plot in Figure introduces an additional layer of valuable information. In other words, Figure shows that, being less exploited, Scaffold 5 would likely be less progressible compared to Scaffold 3.