The yeast two-hybrid (Y2H) system is one of the most technically straightforward, effective, and widely used tools for the discovery of the binary peptide or protein interactions. However, its exceptional detection sensitivity poses a serious challenge for affinity ranking and hence prioritizing the resultant large number of putative interactors for follow-up analyses. To overcome this apparent bottleneck, we describe here a novel yeast growth curve-based interaction-monitoring approach that permits semiautomatic quantification, comparison, and statistically ascertained scoring of a large collection of Y2H interactions under real-time conditions. Initially, we conducted a proof-of-concept test of five literature-validated peptide-protein interactions with known affinities in the low μM range, and subsequently used the method to classify 88 novel vitamin D receptor-binding peptides derived from high-throughput screening of a highly diverse artificial peptide aptamer library. Based on our in-depth data evaluation, we conclude that real-time monitoring of clone growth as a measure of relative binding strength offers a facile, cost-effective, accurate, reproducible, and further adaptable complement to standard Y2H-derived clone management.