Efficient chemical library design for high-throughput virtual screening and drug design requires a pre-screening filter pipeline capable of labeling aggregators, pains, and reos; identifying or excluding covalent binders, flag moieties with specific bio evaluation data; and incorporating physicochemical and pharmacokinetic properties early in the design without compromising the diversity of chemical moieties present in the library. This adaptation of the chemical space results in greater enrichment of hit lists, identified compounds with greater potential for further optimization, and efficient use of computational time. We have implemented a number of medicinal chemistry filters in the KNIME software and analyzed their impact on testing representative libraries with chemoinformatic analysis. We find that the analyzed filters can effectively tailor chemical libraries to a lead-like chemical space, identify protein-protein inhibitor like compounds, prioritize oral bioavailability, identify drug-like compounds, and effectively label unwanted scaffolds or functional groups. However, one should be cautious in their application and carefully study the chemical space suitable for the target and general medicinal chemistry campaign, and review passed and labeled compounds before taking further in silico steps.