1. Can the virtual screening or de-novo design algorithm find molecules that maximize given objectives? Example Benchmarks: Guacamol , VS benchmarks 2. Use CASP tools to predict routes for generated molecules or synthesizabilty scores: Gao et al. 3. Are the generated molecules reasonable? Quality filters like in Guacamol can be applied. ML Publications should always contain visualizations of non-cherry picked, random samples of molecules. 4. Synthesis planning algorithms should be evaluated quantitatively and qualitatively. On the other hand, it is an open question whether small increases on computational benchmarks translate to meaningful improvements in real world drug discovery, where data is often sparse, and projects can operate on quite diverse chemical matter, with distributions shifts, where robustness is key. However, we do not believe experimental validation should ever become a mandatory requirement to benchmark computational methods, as it introduces hard to compare multi-step processes, and not every computational group has the resources for wet-lab validation. We encourage the community to continue to work on refining appropriate benchmarks.