Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and distribution shifts in the operating environment, thus increasing operational risk. Dynamic Assurance of these systems requires augmenting runtime safety components like out-of-distribution detectors and risk estimators. Designing these safety components requires labeled data from failure conditions and risky corner cases that fail the system. However, collecting real-world data of these high-risk scenes can be expensive and sometimes not possible. To address this, there are several scenario description languages with sampling capability for generating synthetic data from simulators to replicate the scenes that are not possible in the real world. Most often, simple search-based techniques like random search and grid search are used as samplers. But we point out three limitations in using these techniques. First, they are passive samplers, which do not use the feedback of previous results in the sampling process. Second, the variables to be sampled may have constraints that need to be applied. Third, they do not balance the tradeoff between exploration and exploitation, which we hypothesize is needed for better coverage of the search space. We present a scene generation workflow with two samplers called Random Neighborhood Search (RNS) and Guided Bayesian Optimization (GBO). These samplers extend the conventional random search and Bayesian Optimization search with the limitation points. We demonstrate our approach using an Autonomous Vehicle case study in CARLA simulation. To evaluate our samplers, we compared them against the baselines of random search, grid search, and Halton sequence search.