Drug discovery campaigns have increasingly adopted DNA-encoded libraries (DELs) in recent years because they allow for relatively cheap and rapid exploration of diverse areas of chemical space. In DELs, a concept first introduced by Brenner and Lerner, scientists sequentially couple small molecules known as building blocks via split-and-pool combinatorial synthesis. The process tags each building block with a unique DNA oligomer such that each final library member will be covalently attached to a record of its synthesis in the form of a sequenceable DNA barcode. Researchers then incubate the entire library with a target of interest and wash away any compounds that do not bind. Finally, experimentalists amplify and sequence the DNA barcodes of the observed binders and further investigate any compounds with detected DNA read counts as potential active compounds. Typically, DELs incorporate two to four cycles of encoding and chemistry, which can achieve a diversity of up to billions of unique compounds. Given the large combinatorial scale of DELs, selection data can be quite noisy due to issues such as variable reaction yields and formation of truncates, as well as errors within exper-imental procedures and noise during DNA sequencing. These sources of noise have made it common to analyze selection data with computational models to prevent wasting time and resources re-synthesizing and evaluating unproductive candidates. Recent work has demonstrated how machine learning approaches can denoise DEL data and identify promising candidates in out-of-sample data. Computational models likely will yield even further insights as they are applied to DEL selection data. In this paper, we introduce a method for analyzing DEL selection data at the building block-level, with the goal of gaining insights we can use to design better DELs for subsequent screening rounds. First, we introduce an interpretable analysis of individual building blocks. Second, we quantify how building blocks interact with each other to affect the activity of an overall compound. Third, leveraging the similar property principle 21 -the idea that similar compounds have similar activity -we demonstrate how we can use similarity scoring methods to predict the productivity of new building blocks and how similarity metrics differ in their ability to identify compounds of similar activity. Finally, we combine the behavior of building blocks at each position into a statistical prediction on the probability of an untested molecule binding to the target of interest.