The process of Drug Discovery is a complex and high risk endeavor that requires focused attention on experimental hypotheses, the application of diverse sets of technologies and data to facilitate high quality decision-making. All is aimed at enhancing the quality of the chemical development candidate(s) through clinical evaluation and into the market. In support of the lead generation and optimization phases of this endeavor, high throughput technologies such as combinatorial/high throughput synthesis and high throughput and ultra-high throughput screening, have allowed the rapid analysis and generation of large number of compounds and data. Today, for every analog synthesized 100 or more data points can be collected and captured in various centralized databases. The analysis of thousands of compounds can very quickly become a daunting task. In this article we present the process we have developed for both analyzing and prioritizing large sets of data starting from diversity and focused uHTS in support of lead generation and secondary screens supporting lead optimization. We will describe how we use informatics and computational chemistry to focus our efforts on asking relevant questions about the desired attributes of a specific library, and subsequently in guiding the generation of more information-rich sets of analogs in support of both processes.