We then evaluated Diff-Shape's performance in the shape-based virtual screening scenario by comparing 3D shape similarity and 2D graph similarity of generations. For comparison, we examined the performance of unconditioned MIDI model as baseline and other shape-conditioned models, SQUID, ShapeMol-NoGuide and ShapeMol-Guide-0.5 in our case. The evaluation was performed on randomly selected 100 molecules from the test set as the reference set. For each reference molecule, 100 valid and unique molecules were generated and assessed. The validity, uniqueness as well as similarity scores of generations are shown in Table . Despite Diff-Shape generations showing lower validity and uniqueness than SQUID, and ShapeMol models, it significantly outperformed SQUID, ShapeMol and MIDI in terms of identifying molecules with 𝑆𝑖𝑚 JK >0.8. Specifically, Diff-Shape achieved a ratio of 0.91 and 0.66 for such molecules with noise level of 0.3 and 0.4 respectively, while SQUID, ShapeMol-NoGuide, ShapeMol-Guide-0.5 and MIDI can only achieve 0.127, 0.001, 0.003 and 0.002, respectively. This highlights that Diff-Shape model does a better job in generating compounds with similar shape, while the drawback of lower validity and uniqueness can be largely solved by sampling larger number of compounds and filtering invalid ones. Considering 2D similarity of the generated set to the reference compound, we filtered molecules by thresholds of 2D similarity 𝑆𝑖𝑚 I with 1.0, 0.7, and 0.3 to gauge the level of novelty in generations regarding to the reference. The results in Table show that the ratio of compound whose 3D shape similarity is larger than 0.8 and 2D similarity is less than 0.7 are 0.830 and 0.641 for Diff-Shape with noise level of 0.3 and 0.4, 0.123 for SQUID, 0.003 and 0.001 for ShapeMol with/without guidance. For compounds whose 3D shape similarity is larger than 0.8 and 2D similarity is less than 0.3, the ratio is 0.213 and 0.353 for Diff-Shape with noise of 0.3 and 0.4, 0.055 for SQUID, 0.003 and 0.001 for ShapeMol with/without guidance. This again demonstrates our method has better chance in generating more diverse compounds with similar shape, comparing with other shape conditioned generation models.