Homomorphic Encryption (HE) is a commonly used tool for building privacy-preserving applications. However, in scenarios with many clients and high-latency networks, communication costs due to large ciphertext sizes are the bottleneck. In this paper, we present a new compression technique that uses an additive homomorphic encryption scheme with small ciphertexts to compress large homomorphic ciphertexts based on Learning with Errors (LWE). Our technique exploits the linear step in the decryption of such ciphertexts to delegate part of the decryption to the server. We achieve compression ratios up to 90% which only requires a small compression key. By compressing multiple ciphertexts simultaneously, we can over 99\% compression rate. Our compression technique can be readily applied to applications which transmit LWE ciphertexts from the server to the client as the response to a query. Furthermore, we apply our technique to private information retrieval (PIR) where a client accesses a database without revealing its query. Using our compression technique, we propose ZipPIR, a PIR protocol which achieves the lowest overall communication cost among all protocols in the literature. ZipPIR does not require any communication with the client in the preprocessing phase, making it a great solution for use cases of PIR with ephemeral clients or high-latency networks.