This paper attempts to empirically assess how advice may reduce suboptimality in a portfolio choice experiment with risk-neutral participants induced via binary-lottery incentives. Previous studies with a larger set of choice tasks report overwhelming evidence of suboptimality and how it is slightly reduced by learning and experience. Participants confront 15 randomly ordered portfolio choices, which they experience again in 2 successive phases. Intermediate advice between phases alerts participants that less-risky investments can improve the outcome for at least one chance event (...) without harming their success chances in the other random event. Compared to the pure choice treatment, another cognitively more demanding treatment additionally asks participants to form event-specific success aspirations that allow us to test satisficing and its optimality. The results show that intermediate advice increases the share of satisficing but not of optimal behavior beyond learning through experience. However, it significantly lowers the average distance from optimality. (shrink)