A major challenge in swarm robotics is efficiently deploying robots into unknown environments, minimising energy and time costs. This is especially important with small aerial robots which have extremely limited flight autonomy. This paper compares three deployment strategies characterised by nominal computation, memory, communication and sensing requirements, and hence are suitable for aerial robots. Energy consumption is decreased by reducing unnecessary flight following two premises: 1) exploiting environmental information gathered by the robots; 2) avoiding diminishing returns and reducing interference between robots. Using a realistic 3-D simulator we examine both energy and time metrics, and also scalability effects. Results indicate that a novel strategy that controls the density of flying robots is most promising in reducing swarm energy costs while maintaining rapid search times. Furthermore, we highlight the energy-time tradeoff and the importance of measuring both metrics, and also the significance of electronics power in calculating total power consumption, even if it is small relative to locomotion power.