A key underlying hypothesis is the derivative compounds bind in a very similar way to their parent hits. Fragmenstein merges fragments by first stitching together the positioned atoms of the parent fragments prior and then locally minimising under strong constraints, without relying on previously generated conformers. To test this the dataset from the Covid Moonshot project was used as it contains a large panel of hit-inspired derivative compounds is available . The Covid Moonshot project was a collaborative SAR-COV-2 protease fragment-based drug discovery project that relied on user submitted ideas of derivative compounds. These submissions were guided by user's choice and as a result represent a spectrum of diverse approaches. The submissions were filtered for compounds that were crystalised and that had a stated parent, yielding a total of 100 compounds. The atomic positions of the conformer from the crystal structure were compared to those of a conformer placed by Fragmenstein constrained against the stated inspiring hits and to those of conformers docked with and without restraints (Figure , interactive at moonshot). The importance of exploiting the structural information of the parent hits is illustrated by the fact that 64% of the proposed merges were found to fully preserve the pose of their parent fragments (mean RMSD<2Å). Fragmenstein was able to propose high-quality poses (RMSD < 1Å) for 28% of the evaluated compounds and acceptable poses (RMSD < 2Å) for 56% of them. Docking (via rDock) was not able to obtain any high-quality poses (Figure ). In order to determine if Fragmenstein was able to better exploit the structural information of the fragment hits than other approaches, we next compared Fragmenstein with the constrained version of rDock using pharmacophoric constraints derived from the parent hits. Figure shows that, while including constraints improves the docking performance, Fragmenstein still outperforms rDock, which was able to produce accurate poses for only 5% of the compounds. A factor involved is that Fragmenstein generates the conformer based on the hits, while docking frequently choses a conformer among a set of generated conformers. Specifically, for this dataset, the most similar generated conformers out of 10, 100 and 1,000 (ETKDG in RDKit) to the crystallographic pose deviated by 0.9 Å, 0.7Å and 0.6 Å on average. The inability to sample a conformer that perfectly matches the crystallographic one underlies the choice in Fragmenstein to start from a stitched-together conformer. This together with the hit-derived strong constraints during minimisation allows the placed molecule to be highly faithful to the parent hits.