Most of the best scoring compounds were docked to the RNA binding groove, at the site occupied by fragments found in PDB structures 5RMM, 5RLZ and 5RLH, the target site defined for this CACHE challenge (Figure ). An exception is CACHE _1454_98, which is predicted to occupy an unrelated binding pocket. In this workflow (WF1454), compounds were docked onto a receptor grid spanning most of the target protein. Six of the eight Round 1 compounds from WF1454 that advanced to Round 2 occupied the RNA-binding groove, one the ATP site and one (CACHE _1454_98) an unrelated site. While top hits from other workflows occupy the expected site, they do not share pharmacophoric features or conserved interactions. CACHE_1414_40 was obtained from growing the crystallized fragment found in the PDB structure 5RMM (Figure ) and is predicted to loosely overlap with the bound fragment. The six best-performing workflows (Figure ) can be divided into three groups (Figure ). WF1414 and WF1438 both adopted strategies where fragments from the PDB were gradually grown and commercial analogs identified along multiple iterative cycles but their implementations were drastically divergent: WF1414 relied on citizen scientists and the gaming interface provided by Foldit to grow fragments, followed by RosettaLigand , a physics-based docking tool, and BCL-AffinityNet, a feed-forward deep neural network, for final scoring ; WF1438 used FEgrow to enumerate fragments in the binding pocket based on a hybrid machine learning (ML) / molecular mechanics energy function leveraging the ANI neural network potential for ligand energetics, and final evaluation with the convolutional neural network scoring function GNINA .