Estimating selection is of key importance in evolutionary biology research. The recent price drop in sequencing and advances in NGS data analysis have opened up new avenues for novel methods that estimate selection quantitatively from time-series allele frequency data. However, it is not yet well understood which method performs best given specific model systems and experimental designs. Here, using popular quantitative metrics, we compared the performance of four prominent methods on a series of simulated data sets and on data from real biological experiments. We identified in three out of four methods the experi- mental conditions best suited for estimating selection. We also explored the limitations of these methods when estimating selection from complex patterns of allele frequency change in some relevant evolutionary scenarios. Our findings highlight the need for modification of population genomics models that are still used in inference of model parameters with the goal to develop new, more accurate methods for the quantitative estimation of selection in time-series genomic data.