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While backpropagation (BP) has achieved widespread success in deep learning, it faces two prominent challenges; that is, computational inefficiency and biological implausibility. These issues arise from the requirements of feedback weight symmetry and the forward/backward pass locking. "Forward learning" (FL), an emerging alternative, updates each layer's weights during the forward pass, eliminating the need for backward error signal propagation to address these concerns. Recent approaches have leveraged contrastive learning as a specialized tool for this scenario. However, it still exhibits suboptimal performance in comparison to BP. Our investigation suggests that existing contrastive FL methods, which assess similarities among local features, are susceptible to the inclusion of task-irrelevant information. In response to this, we propose a straightforward FL objective within a contrastive learning framework, with the goal of enhancing the similarity between local features and label embeddings, i.e., Dictionary Contrastive Forward Learning (DC-FL). Consequently, our objective yields substantial performance improvements, outperforming other state-of-the-art forward learning techniques. Notably, our method closely approaches the performance achieved by BP while concurrently preserving superior memory efficiency.