The use of sophisticated computational methods for de novo design is attractive and deep generative methods have gained a lot of attention. Significant progress has been made when it comes to generating molecules. However, scoring them accurately remains a major challenge. Real-life project experience informs us that in silico predictions (e.g., synthesis, potency, properties) are constantly improving, but they are generally not accurate enough to prioritize a handful compounds for synthesis from a long list of high-scoring AI generated molecules. Thus, what really needs solving is being able to do ultra-accurate predictions to advance the field to the next level. Until then, the approach of biasing molecular design towards compounds similar to known actives will remain as one pragmatic and fruitful way to success.