We present a novel scheme for coordinated search by autonomous agents that is inspired by collective forms of intelligence present in group predators. In the scheme proposed, a group of agents is tasked to find several targets over a search area. The sensing technology is imperfect so there are non-negligible probabilities for false positives and false negatives. Agents maintain two data `trails' across potential target locations that have been explored. One trail is associated with the frequency with which a given location has been probed while the other relates to the current likelihood that a target is present. Each agent independently chooses a decision that is aimed at maximizing the chance of detecting a target without unnecessary duplication in probing. By endowing agents with this simple optimization rule, we show that a form of collective intelligence emerges as agents successfully coordinate indirectly (i.e., they locate all targets) through active manipulation of the trails in a relatively fast fashion. This feature guarantees the proposed scheme is both reconfigurable and scalable.