We further compare our methods under the common molecule optimization setting including two tasks single property constrained optimization and multi-property constrained optimization. Begin- ning with a set of candidate molecules, we aim to optimize the molecular properties while keeping the similarities of the optimized molecules to be as close to the base molecules as possible. The setting is persuasive in many drug discovery tasks where one needs to optimize the properties of a given molecule while keeping the structure similar. . For our reported result, ChemSpacE is manipulating over the latent space learned by MoFlow, as MoFlow leverages gradient-based method which traces the local gradient that leads to property improvement in every step while we take on a more efficient way to learn the global improvement direction and follow it for all steps, we are performing surprisingly well and even better than the gradient-based method used in MoFlow. This empirically supports our assumption about the latent space exploration.