Gao and Coley 33 evaluated the synthesizability of compounds proposed by generative models using ASKCOS, 34 a computer-aided retrosynthetic analysis tool. They showed that distribution learning methods, which learn the probability distribution of the training data, generate compounds predicted to be synthesizable at a frequency similar to that of the training data. However, for goal-directed generation methods, which aim to optimize compounds toward a specific goal, they found that the predicted synthesizability can be compromised in certain cases, even when heuristic synthesizability scores are incorporated into the objective function. As the authors note, the identification of a synthetic pathway using ASKCOS may not accurately reflect the actual synthesizability of a compound, as the retrosynthetic analysis is imperfect and sensitive to the databases and settings used. Nevertheless, their study provides valuable insights into the critical issue of synthesizability and highlights the need for developing methods that can optimize specific properties of compounds while simultaneously ensuring their synthetic accessibility.