In this work, we present a two-stage method for speaker extraction under reverberant and noisy conditions. Given a reference signal of the desired speaker, the clean, but the still reverberant, desired speaker is first extracted from the noisy-mixed signal. In the second stage, the extracted signal is further enhanced by joint dereverberation and residual noise and interference reduction. The proposed architecture comprises two sub-networks, one for the extraction task and the second for the dereverberation task. We present a training strategy for this architecture and show that the performance of the proposed method is on par with other state-of-the-art (SOTA) methods when applied to the WHAMR! dataset. Furthermore, we present a new dataset with more realistic adverse acoustic conditions and show that our method outperforms the competing methods when applied to this dataset as well.