Sampling on the dependent variable is unlikely to be an effective way to learn and develop strategy. Even so, organizations spend millions of dollars on processes such as appreciative inquiry that make inferences about how to adapt their strategies, routines, and practices based upon only successful examples. Two common techniques to this learning process are searching solely for successful solutions and reframing search problems (e.g., unconditionally positive questions). We built a computational model by formalizing appreciative inquiry and comparing it with other similar processes to understand their relative effectiveness. The organizations simulated in our computational model almost always improved performance over time despite learning solely from successful observations. Their relative effectiveness depended on the complexity of the problems, the number of iterations of learning, and how much the learning process preserved variety in potential solutions. These findings suggest that appreciative inquiry may be most effective when people consider the cost and complexity of organizational problems before engaging in the learning process and adapting the process accordingly. These findings also contribute to research on organizational learning by explaining why learners may benefit from structuring the way they communicate as they search, why reframing performance measures may dissolve search problems, and how designed organizational search enables managers to be more deliberate about organizational learning.