Sampling based motion planning methods have been highly successful in solving many high degree of freedom motion planning problems arising in diverse application domains such as traditional robotics, computer-aided design, and computational biology and chemistry. Recent work in metrics for sampling based planners provide tools to analyze the model building process at three levels of detail: sample level, region level, and global level. These tools are useful for comparing the evolution of sampling methods, and have shown promise to improve the process altogether , , . Here, we introduce a filtering strategy for the Probabilistic Roadmap Methods (PRM) with the aim to improve roadmap construction performance by selecting only the samples that are likely to produce roadmap structure improvement. By measuring a new sample's maximum potential structural improvement with respect to the current roadmap, we can choose to only accept samples that have an adequate potential for improvement. We show how this approach can improve the standard PRM framework in a variety of motion planning situations using popular sampling techniques.