The article explores various methods to optimize Elasticsearch searches, emphasizing the importance of filters in enhancing search performance. Filters, which cache results and accelerate searches, can be strategically placed to minimize the computational cost of queries. The article advises using filters before queries to reduce the search space and recommends the use of "bool" filters for combining multiple conditions. It discusses the significance of cacheable filters and their role in expediting searches by reducing the workload on non-cacheable filters. Additionally, the article highlights the potential cost of aggregations, suggesting the use of filter aggregations where possible and cautioning against deep pagination due to the associated computational expense. It also touches on the need for proper text analysis and mapping to avoid inefficient searches, recommending against the use of scripts during searches due to their performance impact. The article concludes by advising careful consideration and testing of optimizations to identify and address true bottlenecks in search performance.