Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions.Recent advancements in Latent Bayesian Optimization (LBO) have shown promise by integrating generative models such as variational autoencoders (VAEs) to manage the complexity of high-dimensional and structured data spaces.However, existing LBO approaches often suffer from the value discrepancy problem, which arises from the reconstruction gap between input and latent spaces.This value discrepancy problem propagates errors throughout the optimization process, leading to suboptimal outcomes.To address this issue, we propose a Normalizing Flow-based Bayesian Optimization (NF-BO), which utilizes normalizing flow as a generative model to establish one-to-one function between input and latent spaces, eliminating the reconstruction gap.Specifically, we introduce SeqFlow, an autoregressive normalizing flow for sequence data.In addition, we develop a new candidate sampling strategy that dynamically adjusts the exploration probability for each token based on its importance.Through extensive experiments, our NF-BO method demonstrates superior performance in molecule generation tasks, significantly outperforming both traditional and recent LBO approaches.