Screening synthetic combinatorial libraries, such as mixtures of oligo(N-substituted)glycines, facilitates rapid drug lead discovery and optimization by vastly increasing the number of candidate molecules made and tested. Discovery efficiency and productivity can be further improved by using experimental design to maximize molecular diversity for a given library size or to bias the library with key features for a specific receptor. We describe new methods to quantify molecular diversity using descriptors that characterize lipophilicity, shape and branching, chemical functionality, and specific binding features. Experimental design methods select sets of side chains that are diverse in these properties, and "flower plots" allow the diversity to be graphically compared. We also quantify the overall diversity accessible to different families of combinatorial chemistry.