diff --git a/R/request.R b/R/request.R
index 98d48d3..c3a9c6e 100644
--- a/R/request.R
+++ b/R/request.R
@@ -90,6 +90,16 @@ as_onet_tibble <- function(x) {
  if (length(x) == 0) {
     return(tibble())
   }
+  
+  # Handle NULL columns by converting them to NA vectors
+  # This prevents as_tibble() from erroring on NULL values
+  if (is.list(x) && !is.data.frame(x)) {
+    # For named lists, replace NULL values with NA
+    x <- lapply(x, function(col) {
+      if (is.null(col)) NA else col
+    })
+  }
+  
   tbl <- as_tibble(x)
   names(tbl) <- to_snake_case(names(tbl))
   tbl
diff --git a/R/search.R b/R/search.R
index f02cc01..f5d1301 100644
--- a/R/search.R
+++ b/R/search.R
@@ -10,7 +10,6 @@
 #'   \describe{
 #'     \item{code}{O*NET-SOC occupation code}
 #'     \item{title}{Occupation title}
-#'     \item{relevance_score}{Search relevance score (if keyword search)}
 #'   }
 #'
 #' @export
@@ -33,8 +32,7 @@ onet_search <- function(keyword, start = 1, end = 20) {
   # Define expected schema for empty results
   schema <- empty_tibble(
     code = character(),
-    title = character(),
-    relevance_score = numeric()
+    title = character()
   )
 
   # Extract data with schema
@@ -44,17 +42,9 @@ onet_search <- function(keyword, start = 1, end = 20) {
   }
 
   results <- map(resp$occupation, \(x) {
-    # Try multiple field names for relevance score
-    relevance <- x$relevance_score %||% 
-                 x$relevanceScore %||% 
-                 x$relevance %||% 
-                 x$score %||% 
-                 NA_real_
-    
     tibble(
       code = x$code %||% NA_character_,
-      title = x$title %||% NA_character_,
-      relevance_score = relevance
+      title = x$title %||% NA_character_
     )
   }) |> list_rbind()
 
diff --git a/README.md b/README.md
index 0f15b7d..cc001b0 100644
--- a/README.md
+++ b/README.md
@@ -72,12 +72,12 @@ library(onet2r)
 # Search for software-related occupations
 results <- onet_search("software developer")
 results
-#> # A tibble: 20 × 3
-#>    code       title                                  relevance_score
-#>    <chr>      <chr>                                            <dbl>
-#>  1 15-1252.00 Software Developers                               100 
-#>  2 15-1251.00 Computer Programmers                               95
-#>  3 15-1256.00 Software Quality Assurance Analysts…               92
+#> # A tibble: 20 × 2
+#>    code       title                                 
+#>    <chr>      <chr>                                 
+#>  1 15-1252.00 Software Developers                   
+#>  2 15-1251.00 Computer Programmers                  
+#>  3 15-1256.00 Software Quality Assurance Analysts…  
 #> # ℹ 17 more rows
 
 # Search by O*NET-SOC code
@@ -185,8 +185,8 @@ onet_skills("invalid-code")
 results <- onet_search("xyzabc123nonexistent")
 #> No occupations found for keyword: "xyzabc123nonexistent"
 results
-#> # A tibble: 0 × 3
-#> # ℹ 3 variables: code <chr>, title <chr>, relevance_score <dbl>
+#> # A tibble: 0 × 2
+#> # ℹ 2 variables: code <chr>, title <chr>
 ```
 
 API errors are automatically handled with:
@@ -217,8 +217,7 @@ library(dplyr)
 
 # Find occupations related to "data"
 results <- onet_search("data analyst") |>
-  filter(relevance_score > 80) |>
-  arrange(desc(relevance_score))
+  arrange(title)
 
 # Get skills for multiple occupations
 codes <- c("15-1252.00", "15-1251.00")
diff --git a/man/onet_search.Rd b/man/onet_search.Rd
index 1187453..0b60b88 100644
--- a/man/onet_search.Rd
+++ b/man/onet_search.Rd
@@ -18,7 +18,6 @@ A tibble with columns:
 \describe{
 \item{code}{O*NET-SOC occupation code}
 \item{title}{Occupation title}
-\item{relevance_score}{Search relevance score (if keyword search)}
 }
 }
 \description{
diff --git a/tests/testthat/test-helpers.R b/tests/testthat/test-helpers.R
index bbd6572..3d1dc9a 100644
--- a/tests/testthat/test-helpers.R
+++ b/tests/testthat/test-helpers.R
@@ -60,6 +60,25 @@ test_that("as_onet_tibble converts column names", {
   expect_equal(result$first_name, "John")
 })
 
+test_that("as_onet_tibble handles NULL columns", {
+  # Test with NULL values in list - simulates API returning NULL for a field
+  input <- list(
+    code = "15-1252.00",
+    title = "Software Developer",
+    not_relevant = NULL,
+    scale_id = "IM"
+  )
+  
+  # Should convert NULL to NA without error
+  result <- onet2r:::as_onet_tibble(input)
+  expect_s3_class(result, "tbl_df")
+  expect_equal(nrow(result), 1)
+  expect_equal(result$code, "15-1252.00")
+  expect_equal(result$title, "Software Developer")
+  expect_true(is.na(result$not_relevant))
+  expect_equal(result$scale_id, "IM")
+})
+
 test_that("extract_list_data handles empty results", {
   # Test with NULL data
   resp_null <- list(occupation = NULL)
diff --git a/vignettes/getting-started.Rmd b/vignettes/getting-started.Rmd
index ae9d0d5..61174bd 100644
--- a/vignettes/getting-started.Rmd
+++ b/vignettes/getting-started.Rmd
@@ -74,7 +74,7 @@ software_jobs <- onet_search("software developer")
 software_jobs
 ```
 
-This returns a tibble with occupation codes, titles, and relevance scores. The results are ordered by relevance to your search term.
+This returns a tibble with occupation codes and titles.
 
 You can control the number of results:
 
@@ -333,8 +333,8 @@ results <- onet_search("xyznonexistent123")
 
 # Still returns a properly structured tibble
 results
-#> # A tibble: 0 × 3
-#> # ℹ 3 variables: code <chr>, title <chr>, relevance_score <dbl>
+#> # A tibble: 0 × 2
+#> # ℹ 2 variables: code <chr>, title <chr>
 
 # You can safely use it in pipelines
 nrow(results)  #> [1] 0
@@ -365,8 +365,7 @@ library(tidyr)
 library(ggplot2)
 
 # Find healthcare occupations and analyze skills
-healthcare <- onet_search("healthcare", start = 1, end = 20) |>
-  filter(relevance_score > 70)
+healthcare <- onet_search("healthcare", start = 1, end = 20)
 
 # Get skills for top 5 occupations
 skills <- healthcare |>