Behavioral search drivers allow more information about the behavior of individuals in an environment to be used during selection. In this paper, we examine several selection methods based on de-aggregating the motion of soft robots into behavior vectors used to drive search. We adapt three behavioral search drivers to this task: є-lexicase selection, discovery of objectives by clustering, and novelty search. These methods are compared to age-fitness pareto optimization and random search. We analyze how these search drivers affect the diversity and quality of soft robots that are tasked with moving as far of a distance as possible. Perhaps the most surprising finding is that random search with elitism is competitive with previously published methods. Overall, we find that elitism plays an important role in the ability to find high fitness solutions, and that lexicase selection and discovery of objectives by clustering with elitism tend to produce the most fit solutions.