Retailers engaging in revenue management rarely implement theoretically optimal prices from an optimisation system directly. Rather, they adjust these with business rules – simple empirical guidelines derived from best practices – such as using discrete price points ending in ‘9’. Similarly, competing objectives of maximizing sales or store visits are regularly considered, which may contrast the profit optimal solution. Although these rules obviously constrain the solution space for the price optimization, little is known about their consequences on overall profits. This study provides an empirical analysis on the impact of commonly used business rules of using (i) discrete price points, (ii) maximum price moves, (iii) corridor pricing and (iv) passive pricing on the size and the quality of the problem’s solution space and their monetary impact. As expected, we find that each additional business rule further constrains the solution space, offering fewer valid price vectors. However, while the combinations of multiple rules substantially reduce the solution space and yields suboptimal solutions that deviate up to 20 per cent from the profit maximum, the application of only individual rules will still provide some optimal solutions. At the same time, business rules enable the estimation of larger assortment subcategories, which allow results more representative for retail practices. This suggests that price vectors which reflect business rules allow not only an increased adherence to business reality, but may lead to little or no deviation from the optimal solution for larger assortments than in unconstrained optimization systems.