We applied theoretical and simulation-based approaches to characterize how microbial community structure influences the amount of sequencing effort to reconstruct metagenomes that are assembled from short-read sequences. First, a coupon collector equation was proposed as an analytical model for predicting sequencing effort as a function of microbial community structure. Characterization was performed by varying community structure properties such as richness, evenness, and genome size. Simulations demonstrated that while community richness and evenness influenced the sequencing effort required to sequence a community metagenome to exhaustion, the effort necessary to sequence an individual genome to a target fraction of exhaustion depended only on the relative abundance of the genome and its genome size. A second analysis evaluated the quantity, completion, and contamination of metagenome-assembled genomes (MAGs) as a function of sequencing effort on four preexisting sequence read data sets from different environments. These data sets were subsampled to various degrees of completeness to simulate the effect of sequencing effort on MAG retrieval. Modeling suggested that sequencing efforts beyond what is typical in published experiments (1 to 10 Gbp) would generate diminishing returns in terms of MAG binning. A software tool, Genome Relative Abundance to Sequencing Effort (GRASE), was created to assist investigators to further explore this relationship. Reevaluation of the relationship between sequencing effort and binning success in the context of genome relative abundance, as opposed to base pairs, provides a constraint on sequencing experiments based on the relative abundance of microbes in an environment rather than arbitrary levels of sequencing effort.