In various different cancer types, target driven resistance to anticancer therapeutics represents a major hurdle in the road to successful cancer remission. Understanding and quantifying drug resistance has been the primary focus of cancer research. However, research efforts are limited by the fact that resistance can conventionally only be studied on a case-by-case basis. Retrospectively studying the drug resistance mutations that drive patient relapse is a way of understanding drug resistance, but has limited applicability in predicting which mutants will be the most clinically relevant. To make matters worse, the treatment landscape of cancer is constantly changing, with new drugs, new targets, and new drug-target combinations being tested in in vitro experiments and in clinical trials. Changing the drug-target combinations alters the repertoire of mutants that may cause treatment relapse. In the constantly shifting treatment landscape of cancer, all possible variants are variants of unknown drug resistance. Thus, there is a pressing need for methods than can systematically quantify drug resistance at-scale, instead of on a case-by-case basis. This dissertation describes a method that can systematically and accurately quantify drug resistance for thousands of mutants in a single experiment. Using this method, 97.6% of all possible variants of unknown drug resistance in a part of the ABL kinase are quantified. This work addresses some of the key technical barriers that limit current genomics techniques that study drug resistance in cancer. In particular, significant advances are made in developing scalable error-corrected sequencing techniques for deep mutational scanning (chapter 2), in comparing and contrasting data from different functional genomics toolkits (chapter 3), and in using genomics to triage therapeutics hypotheses about a rare variant of unknown drug resistance in the Anaplastic Lymphoma Kinase gene (chapter 4). The techniques described here represent important first steps for scalable and prospective characterization of drug resistance to targeted anticancer therapeutics.