Markets for electronic goods provide the possibility of exploring new and more complex pricing schemes, due to the flexibility of information goods and negligible marginal cost. In this paper we compare dynamic performance across price schedules of varying complexity. We provide a monopolist producer with two machine learning methods which implement a strategy that balances exploitation to maximize current profits against exploration to improve future profits. We find that the complexity of the price schedule affects both the amount of exploration necessary and the aggregate profit received by a producer. In general, simpler price schedules are more robust and give up less profit during the learning periods even though the more complex schedules have higher long-run profits. These results hold for both learning methods, even though the relative performance of the methods is quite sensitive to differences in the smoothness of the profit landscape for different price schedules. Our results have implications for automated learning and strategic pricing in non-stationary environments, which arise when the consumer population changes, individuals change their preferences, or competing firms change their strategies.