Unmanned Aerial Vehicles (UAVs) have attracted significant interest in recent years, as they have shown to be effective in supporting a wide range of applications in many different areas, including logistics, search and rescue (SAR) [3], public safety communications, [8], infrastructure monitoring [9], precision agriculture [4], forestry [5], and telecommunications [2]. Specifically we focus on those of search and exploration in the context of search and rescue. In our presented work, success is measured in an agents ability to find all transmitters in as small a time as possible. Through the use of a challenging discretized simulation environment, we investigate the practicality of an empowerment-driven exploration behaviour (EEB) in order to locate an unknown number of wireless transmitters with minimal prior knowledge about the locations of obstacles, transmitters and their properties. With problem specific adaptations to the algorithm, including the ability to detect non-identifying signals from transmitters, when compared with a random walk agent and an idealistic Bayesian agent, the empowerment algorithm performs near to that of the Bayesian agent with unrealistic information about the environment. We show that our empowerment-driven algorithm has practical potential and lays a foundation for future work in this area.