Traditionally in genetic case-control studies controls have been screened to exclude subjects with a personal history of illness. This control group has the advantage of optimal power to detect loci involved in illness, but requires more work and may incur substantial cost in recruitment. An alternative approach to screening is to use unscreened controls sampled from the general population. Such controls are generally plentiful and inexpensive, but in general there is a risk that some may have the same disease as the cases, which will reduce power to detect associations. We have quantified the extent of this power loss, and produced mathematical formulae for the number of unscreened controls necessary to achieve the same power as a fixed sample of screened controls. The effect of using unscreened controls will also depend on the ratio of the number of screened controls to cases specified in the original study design, and this is also investigated. We have also investigated the cost-benefits of the screened and unscreened approaches, according to variation in the relative costs of sampling screened and unscreened controls, together with genotyping costs. We have, thus, identified the range of situations in which using unscreened controls is a cost-effective alternative to the screened control method and could be considered when designing a study. In many of the typical, real-world situations in complex genetics, the use of unscreened controls is potentially cost-effective and can, in general, be considered for disorders with population prevalence Kp < 0.2. With the steady reduction in genotyping costs and the availability of common sets of “population controls” this design is likely to become increasingly cost effective.