In this presentation we first use a framework for deep-learning explainability to identify the most important Reynolds-stress (Q) events in a turbulent channel (simulated via DNS) and a turbulent boundary layer (obtained experimentally). This objective way to assess importance reveals that the most important Q events are not the ones with the highest Reynolds shear stress. This framework is also used to identify completely new coherent structures, and we find that the most important coherent regions in the flow only have an overlap of 70% with the classical Q events. In the second part of the presentation we use deep reinforcement learning (DRL) to discover completely new strategies of active flow control. We show that DRL applied to a blowing-and-suction scheme significantly outperforms the classical opposition control in a turbulent channel: while the former yields 30% drag reduction, the latter only 20%. We conclude that DRL has tremendous potential for drag reduction in a wide range of complex turbulent-flow configurations.