Effective strategies for mimicking α-helix and β-strand epitopes have been developed, producing valuable inhibitors for some classes of protein-protein interactions (PPIs). However, there are no general strategies for translating loop epitopes into useful PPI inhibitors. In this work, we use the LoopFinder program to identify diverse sets of "hot loops," which are loop epitopes that mediate PPIs. These include loops that are well-suited to mimicry with macrocyclic compounds, and loops that are most similar to variable loops on antibodies and ankyrin repeat proteins. We present data-driven criteria for scoring loop-mediated PPIs, uncovering a trove of potentially druggable interactions. We also use unbiased clustering to identify common structures among the hot loops. To translate these insights into real-world inhibitors, we describe a robust, diversity-oriented strategy for the rapid production and evaluation of cyclized loops. This method is applied to a computationally identified loop in the PPI between stonin2 and Eps15, producing submicromolar inhibitors. The most potent inhibitor is well-structured in water and successfully mimics the native epitope. Overall, these computational and experimental strategies provide new opportunities to design inhibitors for an otherwise intractable set of PPIs.