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Robotic search often involves teleoperating vehicles into unknown environments. In such scenarios, prior knowledge of target location or environmental map may be a viable resource to tap into and control other autonomous robots in the vicinity towards an improved search performance. In this paper, we test the hypothesis that despite having the same skill, prior knowledge of target or environment affects teleoperator actions, and such knowledge can therefore be inferred through robot movement. To investigate whether prior knowledge can improve human-robot team performance, we next evaluate an adaptive mutual-information blending strategy that admits a time-dependent weighting for steering autonomous robots. Human-subject experiments show that several features including distance travelled by the teleoperated robot, time spent staying still, speed, and turn rate, all depend on the level of prior knowledge and that absence of prior knowledge increased workload. Building on these results, we identified distance travelled and time spent staying still as movement cues that can be used to robustly infer prior knowledge. Simulations where an autonomous robot accompanied a human teleoperated robot revealed that whereas time to find the target was similar across all information-based search strategies, adaptive strategies that acted on movement cues found the target sooner more often than a single human teleoperator compared to non-adaptive strategies. This gain is diluted with number of robots, likely due to the limited size of the search environment. Results from this work set the stage for developing knowledge-aware control algorithms for autonomous robots in collaborative human-robot teams.