We propose sequential translation that incrementally grows the set of discovered novel molecules and use the model-discovered molecules as starting points for further search episodes. This regime of starting exploration from states reached in previous episodes was also explored under the setting of RL from image inputs . More concretely, we implement sequential translation with a reinforcement learning policy that operates under the fragment-based action space defined in Section 4.1, while using a moving initial state distribution ρ 0 , which is a distribution over molecules in the frontier set F = I ∪ C. By starting new search episodes from the frontier set -the union of the initial set and good molecules that are discovered by the RL policy, we achieve efficient search in the chemical space by staying close to the high-quality subspace and achieve novel molecule generation through a sequence of fragment-based editing operations to the known molecules. Our proposed search algorithm is detailed in Algorithm 1. Let y be the fragment designated by e, delete fragment x ← x -y 25: