Deep Neural Networks (DNNs) are known to be brittle to even minor distribution shifts compared to the training distribution . Simplicity Bias (SB) of DNNs – bias towards learning a small number of simplest features – has been demonstrated to be a key reason for this brittleness. Prior works have shown that the effect of Simplicity Bias is extreme – even when the features learned are diverse, training the classification head again selects only few of the simplest features, leading to similarly brittle models. In this work, we introduce Feature Reconstruction Regularizer (FRR) in the linear classification head, with the aim of reducing Simplicity Bias, thereby improving Out-Of-Distribution (OOD) robustness. The proposed regularizer when used during linear layer training, termed as FRR-L, enforces that the features can be reconstructed back from the logit layer, ensuring that diverse features participate in the classification task. We further propose to finetune the full network by freezing the weights of the linear layer trained using FRR-L. This approach, termed as FRR-FLFT or Fixed Linear FineTuning, improves the quality of the learned features, making them more suitable for the classification task. Using this simple solution, we demonstrate up to 12% gain in accuracy on the recently introduced synthetic datasets with extreme distribution shifts. Moreover, on the standard OOD benchmarks recommended on DomainBed, our technique can provide up to 5% gains over the existing SOTA methods .