Evolution is an adaptive walk through a hypothetical fitness landscape, which depicts the relationship between genotypes and the fitness of each corresponding phenotype. We constructed an empirical fitness landscape for a catalytic RNA by combining next-generation sequencing, computational analysis, and "serial depletion," an in vitro selection protocol. By determining the reaction rate constant for every point mutant of a catalytic RNA, we demonstrated that abundance in serially depleted pools correlates with biochemical activity (correlation coefficient r = 0.67, standard score Z = 7.4). Therefore, enumeration of each genotype by deep sequencing yielded a fitness landscape containing similar to 10(7) unique sequences, without requiring measurement of the phenotypic fitness for each sequence. High-throughput mapping between genotype and phenotype may apply to artificial selections, host-pathogen interactions, and other biomedically relevant evolutionary phenomena.