Abstract: The practical usability of de novo small molecule generation depends heavily on the synthesizability of generated molecules.We propose BBO-SYN, a generative framework based on black-box optimization (BBO), which predicts diverse molecules with desired properties together with corresponding synthesis pathways.Given an input molecule A, BBO-SYN employs a state-of-the-art BBO method operating on a latent space of molecules to find a reaction partner B, which maximizes the property score of the reaction product C, as determined by a pre-trained template-free reaction predictor. This single-step reaction (A+B→C) forms the basis for an optimization loop, resulting in a synthesis tree yielding products with high property scores.Empirically, the sampling and search strategy of BBO-SYN outperforms comparable baselines on four synthesis-aware optimization tasks (QED, DRD2, GSK3$\beta$, and JNK3), increasing product diversity by 37% and mean property score by 25% on our hardest JNK3 task.