Agents improve by interacting with an environment and planning. By leveraging information about what they don't know, they can learn better and faster, at least in environments that benefit from exploring. They do this by estimating the uncertainty in their predictions. There are choices for how to estimate the uncertainty, and in this work, we look at what effect this choice has on the exploration and strength of agents playing board games. We compare the effect of a source of uncertainty which perfectly tracks what the agent has seen, and a source which generalizes. We also describe the challenges associated with tuning uncertainty estimators and show what considerations have to be made when exploration is not all you need.