Since the early days of artificial intelligence there has been a constant search for useful techniques to tackle the computational complexity of decision making. By now, it is widely accepted that optimal decision making is in most cases beyond our reach. Herbert Simon’s approach based on satisficing offers a more realistic alternative, but it says little on how to construct satisficing algorithms or systems. In practice, satisficing comes in many different flavors, one of which, bounded optimality, restores a weak form of optimality. This paper demonstrates this form of satisficing in the area of anytime problem-solving and argues that it is a viable approach to formalize the notion of satisficing.