Our experiments demonstrate that our method can efficiently and effectively steer state-of-the-art molecule generative models for molecule manipulation with a small amount of training/inference time, data, and oracle calls. To quantitatively measure the performance of molecule manipulation, we design two new evaluation metrics named strict success rate and relax success rate, which evaluate the percentage of successful manipulations with smooth property-changing molecules over manipulations of a group of molecules. In addition, we compare ChemSpacE with a gradient-based optimization method that traverses the latent space of molecule generative models on the molecule optimization task. To facilitate the interactive molecule design and discovery for practitioners, we further develop an interface for real-time interactive molecule manipulations and smooth molecular structure/property changes. We summarize the main contributions as follows: