We further introduce a novel sequential translation scheme for molecular optimization. We start the molecular search by translating from known active molecules and store the discovered molecules as new potential initialization states for subsequent searches. As a monotonic expansion of molecular graphs may end up producing undesirable, large molecules, we also include the deletion of substructures as a possible action. This enables our method to backtrack to good molecular states and iteratively improve generated molecules during the sequential translation process. Previous works optimize molecules either by generating from scratch or a single translation from known molecules, which is inefficient in finding high-quality molecules and often discovering molecules lacking novelty/diversity. Our proposed framework addresses these deficiencies since our method is (1) very efficient in finding molecules that satisfy property constraints as the model stay close to the high-property-score chemical manifold; and (2) able to produce highly novel molecules because the sequence of fragment-based translation can lead to very different and diverse molecules compared to the known active set.