Identifying the nodes able to drive the state of a network is crucial to understand, and eventually control, biological systems. Despite recent advances, such identification remains difficult because of the huge number of equivalent controllable configurations, even in relatively simple networks. Based on the evidence that in many applications it is essential to test the ability of individual nodes to control a specific target subset, we develop a fast and principled method to identify controllable driver-target configurations in sparse and directed networks. We demonstrate our approach on simulated networks and experimental gene networks to characterize macrophage dysregulation in human subjects with multiple sclerosis. Author Summary We introduce an optimized heuristic, called stepwise target controllability, to quantify the centrality of a candidate driver node to influence the state of a network target set. We use this method to study macrophage gene network alterations in multiple sclerosis. We show that multiple sclerosis is characterized by a global loss of gene coactivation and that this is due to the dysregulation of few molecules along the driver-target pathways. These findings provide new insights into the macrophage network mechanisms underlying the pathophysiology of multiple sclerosis and provide fresh tools for the study of driver-target controllability in complex networked systems.