Despite the rapid decrease in sequencing costs, sequencing a large group of individuals is still prohibitively expensive. Recently, several sophisticated pooling designs were suggested that can identify carriers of rare alleles in large cohorts with a significantly smaller number of lanes, thus dramatically reducing the cost of such large scale genotyping projects. These approaches all use combinatorial pooling designs where each individual is either present in a pool or absent from it. One can then infer the number of carriers in a pool, and by combining information across pools, reconstruct the identity of the carriers. We show that one can gain further efficiency and cost reduction by using “weighted” designs, in which different individuals donate different amounts of DNA to the pools. Intuitively, in this situation the number of mutant reads in a pool does not only indicate the number of carriers, but also of the identity of the carriers. We describe and study a simple but powerful example of such weighted designs, with non-overlapping pools. We demonstrate that even this naive approach is not only easier to implement and analyze but is also competitive in terms of accuracy with combinatorial designs when identifying very rare variants, and is superior to the combinatorial designs when genotyping more common variants. We then discuss how weighting can be further incorporated into existing designs to increase their accuracy and demonstrate the resulting improvement in reconstruction efficiency using simulations. Finally, we argue that these weighted designs have enough power to facilitate detection of common alleles, so they can be used as a cornerstone of whole-exome or even whole-genome sequencing projects.