A main goal of CACHE is to highlight computational strategies that repeatedly perform well within a challenge or across multiple targets. Interestingly, using physics-based docking data on a relatively small library to train a ML model that can then be used to efficiently navigate a much larger chemistry space was a winning strategy both in CACHE #1 (WF1193 and WF1209) and CACHE #2 (WF1454 andWF1456) (Figure ). Among the dozens of commercial and open-source computational tools used by CACHE participants, the convolutional network scoring function implemented in GNINA was found in one winning workflow in CACHE #1 (WF1181) and in two in CACHE #2 (WF1438 and WF1456), strongly suggesting that this software is robust across two targets absent from training sets (no ligand with measurable binding affinity was previously known for either CACHE target). Fragment-based techniques linking docked fragments in CACHE #1 (WF1183 and WF1202) or growing crystallized fragments in CACHE #2 (WF1414, WF1421, and WF1438) also define a recurrently successful approach to computational ligand design. Workflow WF1414 is a distinct variation on this theme in that it relies on the design of citizen scientists who use a gaming interface to grow fragments in a binding pocket after which designs are evaluated with RosettaLigand. Combining human creativity with tools such as RosettaLigand may indeed be a recipe for success.