Furthermore, it remains difficult to generate molecules with desired properties. Previous works tackle the problem with reinforcement learning-based, latent space optimization-based, and searching-based methods to achieve property control of the generated molecules . Specifically, reinforcement learning-based algorithms equip the model with rewards designed to encourage the models to generate molecules with specific molecular properties. Latent space optimization-based algorithms take advantage of the learned latent space of molecule generative models and optimize the molecular properties via Bayesian Optimization . Searching-based algorithms directly search the discrete and high-dimensional chemical space for molecules with optimal properties . However, these works often have three major issues. (1) They require many expensive oracle calls to provide feedback (i.e., property scores) of the intermediate molecules during the searching or optimization process . ( ) They often only focus on the outcome of the process while ignoring its intermediate steps which can be essential for chemists and pharmacologists in understanding the chemical instances and rules that govern the process. (3) They stick to local gradients while putting less focus on global directions in the chemical/latent space.