We introduce a model of strategic experimentation on social networks where forward-looking agents learn from their own and neighborsâ€™ successes. In equilibrium, private discovery is followed by social diffusion. Social learning crowds out own experimentation, so total information decreases with network density; we determine density thresholds below which agents asymptotically learn the state. In contrast, agent welfare is single-peaked in network density, and achieves a second-best benchmark level at intermediate levels that achieve a balance between discovery and diffusion. We also study how learning and welfare differ across directed, undirected and clustered networks.