Genomic Selection (GS) is a breeding technique that utilizes whole genome markers to make trait predictions. The goal of GS is to identify the top candidates that have the most desirable trait values. Usually, GS has been formulated as a regression problem where the marker data is used to predict phenotypic values. However, since the end goal of GS is identification of top candidates, ranking the individuals makes far more sense. Creating accurate ranking models pose three fundamental challenges—presence of noise in phenotypic data, extremely high dimensional nature of the genotypic data and small sample size of the genomic datasets. To combat these challenges, we present a novel two phase approach to increase the noise tolerance of ranking based approaches. The proposed algorithm uses pruning to perform noise filtering and leverages biclustering to improve model generalization. This approach is evaluated on both pointwise and pairwise ranking algorithms. Previous work on Arabidopsis and CIMMYT wheat datasets yielded mean Normalized Discounted Cumulative Gain (NDCG) @10 scores of 0.883 and 0.748 respectively. The proposed approach outperforms these results on both of the datasets yielding ranking accuracies of 0.965 and 0.865 respectively. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.