Abstract: CLIP's 77-token text encoder truncates detailed image descriptions. We present ERCLIP, a small-data adaptation of CLIP for long-caption image--text retrieval. During training, a long caption is split into sentences that the CLIP text encoder encodes independently; the sentence embeddings are fused by mean pooling or a gated residual Transformer scorer and receive their own contrastive loss. At inference ERCLIP is an unmodified CLIP dual encoder: test captions are still read through the 77-token window, and only training sees the full caption. We find that this fine-tuning erodes CLIP's zero-shot classification, most when the fusion output is not a combination of CLIP embeddings, and add a frozen-CLIP preservation loss on the image and short-caption embeddings. With 20k pairs and one epoch, ERCLIP improves long-caption R@1 over CLIP by 6.5--11.2 points, closing 62--77% of the gap to LongCLIP with 2% of its long captions. Preservation recovers 1.5--2.1 ImageNet zero-shot points (30--42% of the loss) and adds up to 2.4 points of COCO image-to-text R@1, at no inference cost.