Within a random set of 2000 scaffolds from ChEMBL, highly desirable, progressible fragments are found to be relatively uncommon (Figure ). In this figure, the green and yellow dots represent scaffold-fragments with the highest frequency of occurrence. Conducting such analysis offers a rapid means of assessing EVP statistics in a given dataset. In addition to selecting the most progressible fragments for a screening library, the EVPs methodology, along with its resulting statistics, holds potential for application in various contexts, such as the identification of unexplored Exit Vectors. For instance, when aiming to identify scaffolds with unexplored vectors, fragments based on Scaffolds 1, 4, and 10 may be the preferable choices.