Abstract The advent of next generation sequencing technologies has made whole-genome and whole-population sampling possible, even for eukaryotes with large genomes. With this development, experimental evolution studies can be designed to observe molecular evolution “in-action” via Evolve-and-Resequence (E&R) experiments. Among other applications, E&R studies can be used to locate the genes and variants responsible for genetic adaptation. Existing literature on time-series data analysis often assumes large population size, accurate allele frequency estimates, and wide time spans. These assumptions do not hold in many E&R studies. In this article, we propose a method-Composition of Likelihoods for Evolve-And-Resequence experiments (C lear )–to identify signatures of selection in small population E&R experiments. C lear takes whole-genome sequence of pool of individuals (pool-seq) as input, and properly addresses heterogeneous ascertainment bias resulting from uneven coverage. C lear also provides unbiased estimates of model parameters, including population size, selection strength and dominance, while being computationally efficient. Extensive simulations show that C lear achieves higher power in detecting and localizing selection over a wide range of parameters, and is robust to variation of coverage. We applied C lear statistic to multiple E&R experiments, including, data from a study of D. melanogaster adaptation to alternating temperatures and a study of outcrossing yeast populations, and identified multiple regions under selection with genome-wide significance.