The current study tested the quantity and quality of human exploration learning in a virtual environment. Given the everyday experience of humans with physical object exploration, we document substantial practice gains in the time, force, and number of actions needed to classify the structure of virtual chains, marking the joints as revolute, prismatic, or rigid. In line with current work on skill acquisition, participants could generalize the new and efficient psychomotor patterns of object exploration to novel objects. On the one hand, practice gains in exploration performance could be captured by a negative exponential practice function. On the other hand, they could be linked to strategies and strategy change. After quantifying how much was learned in object exploration and identifying the time course of practice-related gains in exploration efficiency (speed), we identified what was learned. First, we identified strategy components that were associated with efficient (fast) exploration performance: sequential processing, simultaneous use of both hands, low use of pulling rather than pushing, and low use of force. Only the latter was beneficial irrespective of the characteristics of the other strategy components. Second, we therefore characterized efficient exploration behavior by strategies that simultaneously take into account the abovementioned strategy components. We observed that participants maintained a high level of flexibility, sampling from a pool of exploration strategies trading the level of psycho-motoric challenges with exploration speed. We discuss the findings pursuing the aim of advancing intelligent object exploration by combining analytic (object exploration in humans) and synthetic work (object exploration in robots) in the same virtual environment.