1. We demonstrate a way to learn distributional molecular fragment vocabulary through a VQ-VAE and the effectiveness of the learned vocabulary in molecular optimization. 2. We propose a novel molecular search scheme of sequential translation, which gradually improves the quality and novelty of generation through backtracking and a stored frontier. 3. We implement a novelty/diversity-aware RL policy combining the fragment vocabulary and the sequential translation scheme that significantly outperforms state-of-the-art methods in benchmark single/multi-objective molecular optimization tasks. ing previous atom-level models. However, these models use fixed fragment vocabularies, which are typically small and fixed a priori, limiting the chemical space spanned by the models. In our work, we utilize a learned molecular fragment vocabulary, which is obtained by training a VQ-VAE on a large set of fragments extracted from ChEMBL . By sampling fragments from the learned distribution, our model spans a much larger chemical space than methods using a fixed vocabulary (Figure , Figure ). ) uses Bayesian optimization on the latent space. On the other hand, we train a novelty/diversity-aware RL policy to search for novel, diverse molecules that retain desired properties. Our method also initializes searches from model-discovered molecules, which greatly improves the efficiency and diversity of the generated molecules. Given the enormous distributional fragment vocabulary that we use in the RL process, search strategies such as random mutation used in genetic algorithms are likely to be very inefficient.