This paper studies the problem of adaptively searching for an unknown target using multiple agents connected through a time-varying network topology. Agents are equipped with sensors capable of fast information processing, and we propose an asynchronous decentralized algorithm for controlling their search based on noisy observations. We propose asynchronous decentralized algorithms for adaptive query-based search that combine the Bayesian bisection method and social learning. Under standard assumptions on the time-varying network dynamics, we prove convergence to correct consensus on the value of the parameter as the number of iterations grow. Our results establish that stability and consistency can be maintained even with one-way updating and randomized pairwise averaging, thus providing a scalable low complexity alternative to the synchronous decentralized estimation algorithms studied in previous works. We illustrate the effectiveness and robustness of our algorithm for random network topologies.